Search Results for author: Han Liu

Found 275 papers, 79 papers with code

Surrogate Signals from Format and Length: Reinforcement Learning for Solving Mathematical Problems without Ground Truth Answers

1 code implementation26 May 2025 Rihui Xin, Han Liu, Zecheng Wang, Yupeng Zhang, Dianbo Sui, Xiaolin Hu, Bingning Wang

The resulting GRPO approach, leveraging format-length surrogate signals, not only matches but surpasses the performance of the standard GRPO algorithm relying on ground truth answers in certain scenarios, achieving 40. 0\% accuracy on AIME2024 with a 7B base model.

Logical Reasoning Mathematical Problem-Solving

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions

no code implementations26 May 2025 Jerry Yao-Chieh Hu, Xiwen Zhang, Maojiang Su, Zhao Song, Han Liu

We study the computational limits of learning $k$-bit Boolean functions (specifically, $\mathrm{AND}$, $\mathrm{OR}$, and their noisy variants), using a minimalist single-head softmax-attention mechanism, where $k=\Theta(d)$ relevant bits are selected from $d$ inputs.

A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization

no code implementations22 May 2025 Ziqing Wang, Kexin Zhang, Zihan Zhao, Yibo Wen, Abhishek Pandey, Han Liu, Kaize Ding

Large language models (LLMs) are introducing a paradigm shift in molecular discovery by enabling text-guided interaction with chemical spaces through natural language, symbolic notations, with emerging extensions to incorporate multi-modal inputs.

Survey

Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought

no code implementations21 May 2025 Ao Liu, Botong Zhou, Can Xu, Chayse Zhou, Chenchen Zhang, Chengcheng Xu, Chenhao Wang, Decheng Wu, Dengpeng Wu, Dian Jiao, Dong Du, Dong Wang, Feng Zhang, Fengzong Lian, Guanghui Xu, Guanwei Zhang, Hai Wang, Haipeng Luo, Han Hu, Huilin Xu, Jiajia Wu, Jianchen Zhu, Jianfeng Yan, Jiaqi Zhu, Jinbao Xue, Jun Xia, Junqiang Zheng, Kai Liu, Kai Zhang, Kai Zheng, Kejiao Li, Keyao Wang, Lan Jiang, Lixin Liu, Lulu Wu, Mengyuan Huang, Peijie Yu, Peiqi Wang, Qian Wang, Qianbiao Xiang, Qibin Liu, Qingfeng Sun, Richard Guo, Ruobing Xie, Saiyong Yang, Shaohua Chen, Shihui Hu, Shuai Li, Shuaipeng Li, Shuang Chen, Suncong Zheng, Tao Yang, Tian Zhang, TingHao Yu, Weidong Han, Weijie Liu, Weijin Zhou, Weikang Wang, Wesleye Chen, Xiao Feng, Xiaoqin Ren, Xingwu Sun, Xiong Kuang, Xuemeng Huang, Xun Cao, Yanfeng Chen, Yang Du, Yang Zhen, Yaping Deng, Yi Shen, Yigeng Hong, Yiqi Chen, Yiqing Huang, Yuchi Deng, Yue Mao, Yulong Wang, Yuyuan Zeng, Zenan Xu, Zhanhui Kang, Zhenxiang Yan, Zheng Fang, Zhichao Hu, Zhongzhi Chen, Zhuoyu Li, Zongwei Li, Alex Yan, Ande Liang, Baitong Liu, Beiping Pan, Bin Xing, Binghong Wu, Bingxin Qu, Bolin Ni, Boyu Wu, Chen Li, Cheng Jiang, Cheng Zhang, Chengjun Liu, Chengxu Yang, Chiyu Wang, Chong Zha, Daisy Yi, Di Wang, Fanyang Lu, Fei Chen, Feifei Liu, Feng Zheng, Guanghua Yu, Guiyang Li, Guohua Wang, Haisheng Lin, Han Liu, Han Wang, Hao Fei, Hao Lu, Haoqing Jiang, Haoran Sun, Haotian Zhu, Huangjin Dai, Huankui Chen, Huawen Feng, Huihui Cai, Huxin Peng, Jackson Lv, Jiacheng Shi, Jiahao Bu, Jianbo Li, Jianglu Hu, Jiangtao Guan, Jianing Xu, Jianwei Cai, Jiarong Zhang, Jiawei Song, Jie Jiang, Jie Liu, Jieneng Yang, Jihong Zhang, Jin lv, Jing Zhao, Jinjian Li, JinXing Liu, Jun Zhao, Juntao Guo, Kai Wang, Kan Wu, Lei Fu, Lei He, Lei Wang, Li Liu, Liang Dong, Liya Zhan, Long Cheng, Long Xu, Mao Zheng, Meng Liu, Mengkang Hu, Nanli Chen, Peirui Chen, Peng He, Pengju Pan, Pengzhi Wei, Qi Yang, Qi Yi, Roberts Wang, Rongpeng Chen, Rui Sun, Rui Yang, Ruibin Chen, Ruixu Zhou, Shaofeng Zhang, Sheng Zhang, Shihao Xu, Shuaishuai Chang, Shulin Liu, Siqi Wang, Songjia Feng, Songling Yuan, Tao Zhang, Tianjiao Lang, Tongkai Li, Wei Deng, Wei Li, Weichao Wang, Weigang Zhang, Weixuan Sun, Wen Ouyang, Wenxiang Jiao, Wenzhi Sun, Wenzhuo Jia, Xiang Zhang, Xiangyu He, Xianshun Ren, Xiaoying Zhu, Xiaolong Guo, Xiaoxue Li, Xiaoyu Ma, Xican Lu, Xinhua Feng, Xinting Huang, Xinyu Guan, Xirui Li, Xu Zhang, Xudong Gao, Xun Luo, Xuxiang Qi, Yangkun Chen, Yangyu Tao, Yanling Xiao, Yantao Mai, Yanze Chen, Yao Ding, Yeting Yang, YiFan Song, Yifan Yang, Yijiao Zhu, Yinhe Wu, Yixian Liu, Yong Yang, Yuanjun Cai, Yuanlin Tu, Yue Zhang, Yufei Huang, YuHang Zhou, Yuhao Jiang, Yuhong Liu, Yuhui Hu, YuJin Lin, Yun Yang, Yunhao Wang, Yusong Zhang, Zekun Wu, Zelong Zhang, Zhan Yu, Zhaoliang Yang, Zhe Zhao, Zheng Li, Zhenyu Huang, Zhiguang Liu, Zhiqing Kui, Zhiyin Zeng, Zhiyuan Xiong, Zhuo Han, Zifan Wu, Zigang Geng, Zilong Zhao, Ziyan Tang, Ziyuan Zhu, Zonglei Zhu, Zhijiang Xu

As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model.

Chatbot Instruction Following +2

Fast and Low-Cost Genomic Foundation Models via Outlier Removal

1 code implementation1 May 2025 Haozheng Luo, Chenghao Qiu, Maojiang Su, Zhihan Zhou, Zoe Mehta, Guo Ye, Jerry Yao-Chieh Hu, Han Liu

Unlike existing GFM benchmarks, GERM offers the first comprehensive evaluation framework to systematically assess the vulnerability of GFMs to adversarial attacks.

Adversarial Attack Adversarial Robustness +7

Attention Mechanism, Max-Affine Partition, and Universal Approximation

no code implementations28 Apr 2025 Hude Liu, Jerry Yao-Chieh Hu, Zhao Song, Han Liu

We establish the universal approximation capability of single-layer, single-head self- and cross-attention mechanisms with minimal attached structures.

Universal Approximation with Softmax Attention

1 code implementation22 Apr 2025 Jerry Yao-Chieh Hu, Hude Liu, Hong-Yu Chen, Weimin Wu, Han Liu

We prove that with linear transformations, both (i) two-layer self-attention and (ii) one-layer self-attention followed by a softmax function are universal approximators for continuous sequence-to-sequence functions on compact domains.

MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse

1 code implementation24 Mar 2025 Zhenyu Pan, Han Liu

We present MetaSpatial, the first reinforcement learning (RL)-based framework designed to enhance 3D spatial reasoning in vision-language models (VLMs), enabling real-time 3D scene generation without the need for hard-coded optimizations.

Layout Generation Reinforcement Learning (RL) +2

AI-assisted Early Detection of Pancreatic Ductal Adenocarcinoma on Contrast-enhanced CT

1 code implementation13 Mar 2025 Han Liu, Riqiang Gao, Sasa Grbic

However, due to the lack of early and disease-specific symptoms, most patients with PDAC are diagnosed at an advanced disease stage.

A Non-contrast Head CT Foundation Model for Comprehensive Neuro-Trauma Triage

no code implementations28 Feb 2025 Youngjin Yoo, Bogdan Georgescu, Yanbo Zhang, Sasa Grbic, Han Liu, Gabriela D. Aldea, Thomas J. Re, Jyotipriya Das, Poikavila Ullaskrishnan, Eva Eibenberger, Andrei Chekkoury, Uttam K. Bodanapally, Savvas Nicolaou, Pina C. Sanelli, Thomas J. Schroeppel, Yvonne W. Lui, Eli Gibson

Recent advancements in AI and medical imaging offer transformative potential in emergency head CT interpretation for reducing assessment times and improving accuracy in the face of an increasing request of such scans and a global shortage in radiologists.

Anatomy Diagnostic

Can Domain Experts Rely on AI Appropriately? A Case Study on AI-Assisted Prostate Cancer MRI Diagnosis

no code implementations3 Feb 2025 Chacha Chen, Han Liu, Jiamin Yang, Benjamin M. Mervak, Bora Kalaycioglu, Grace Lee, Emre Cakmakli, Matteo Bonatti, Sridhar Pudu, Osman Kahraman, Gul Gizem Pamuk, Aytekin Oto, Aritrick Chatterjee, Chenhao Tan

Building on existing tools for teaching prostate cancer diagnosis, we develop an interface and conduct two experiments to study how AI assistance and performance feedback shape the decision making of domain experts.

Decision Making

Self Pre-training with Adaptive Mask Autoencoders for Variable-Contrast 3D Medical Imaging

no code implementations15 Jan 2025 Badhan Kumar Das, Gengyan Zhao, Han Liu, Thomas J. Re, Dorin Comaniciu, Eli Gibson, Andreas Maier

To address this limitation, we propose a 3D Adaptive Masked Autoencoders (AMAE) architecture that accommodates a variable number of 3D input contrasts per subject.

Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation

1 code implementation13 Jan 2025 Han Liu, Yinwei Wei, Fan Liu, Wenjie Wang, Liqiang Nie, Tat-Seng Chua

In this paper, we develop a novel meta-learning-based multimodal fusion framework called Meta Multimodal Fusion (MetaMMF), which dynamically assigns parameters to the multimodal fusion function for each micro-video during its representation learning.

Meta-Learning Multimodal Recommendation +1

A Text-Based Knowledge-Embedded Soft Sensing Modeling Approach for General Industrial Process Tasks Based on Large Language Model

no code implementations9 Jan 2025 Shuo Tong, Han Liu, Runyuan Guo, Xueqiong Tian, Wenqing Wang, Ding Liu, Youmin Zhang

To address these challenges, we propose a general framework named LLM-TKESS (large language model for text-based knowledge-embedded soft sensing), harnessing the powerful general problem-solving capabilities, cross-modal knowledge transfer abilities, and few-shot capabilities of LLM for enhanced soft sensing modeling.

Language Modeling Language Modelling +5

Do Code LLMs Understand Design Patterns?

no code implementations8 Jan 2025 Zhenyu Pan, Xuefeng Song, Yunkun Wang, Rongyu Cao, Binhua Li, Yongbin Li, Han Liu

Code Large Language Models (LLMs) demonstrate great versatility in adapting to various downstream tasks, including code generation and completion, as well as bug detection and fixing.

Code Generation

A Soft Sensor Method with Uncertainty-Awareness and Self-Explanation Based on Large Language Models Enhanced by Domain Knowledge Retrieval

no code implementations6 Jan 2025 Shuo Tong, Han Liu, Runyuan Guo, Wenqing Wang, Xueqiong Tian, Lingyun Wei, Lin Zhang, Huayong Wu, Ding Liu, Youmin Zhang

To achieve this, we propose a novel framework called the Few-shot Uncertainty-aware and self-Explaining Soft Sensor (LLM-FUESS), which includes the Zero-shot Auxiliary Variable Selector (LLM-ZAVS) and the Uncertainty-aware Few-shot Soft Sensor (LLM-UFSS).

In-Context Learning Sensor Modeling +2

Transformers Simulate MLE for Sequence Generation in Bayesian Networks

no code implementations5 Jan 2025 Yuan Cao, Yihan He, Dennis Wu, Hong-Yu Chen, Jianqing Fan, Han Liu

We demonstrate that there exists a simple transformer model that can (i) estimate the conditional probabilities of the Bayesian network according to the context, and (ii) autoregressively generate a new sample according to the Bayesian network with estimated conditional probabilities.

Learning Spectral Methods by Transformers

no code implementations2 Jan 2025 Yihan He, Yuan Cao, Hong-Yu Chen, Dennis Wu, Jianqing Fan, Han Liu

In this work, we study the capacities of Transformers in performing unsupervised learning.

In-Context Learning

Improving Accuracy and Calibration via Differentiated Deep Mutual Learning

no code implementations CVPR 2025 Han Liu, Peng Cui, Bingning Wang, WeiPeng Chen, Yupeng Zhang, Jun Zhu, Xiaolin Hu

Deep Neural Networks (DNNs) have achieved remarkable success in a variety of tasks, particularly in terms of prediction accuracy.

Diversity

AlignAb: Pareto-Optimal Energy Alignment for Designing Nature-Like Antibodies

no code implementations30 Dec 2024 Yibo Wen, Chenwei Xu, Jerry Yao-Chieh Hu, Han Liu

We present a three-stage framework for training deep learning models specializing in antibody sequence-structure co-design.

Language Modeling Language Modelling

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism

no code implementations30 Dec 2024 Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu, Han Liu, Mladen Kolar

Despite this dilemma, the common practice of choosing batch sizes in language model training often prioritizes training efficiency -- employing either constant large sizes with data parallelism or implementing batch size warmup schedules.

FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing

no code implementations16 Dec 2024 Zekai Li, Jintu Zheng, Ji Liu, Han Liu, Haowei Zhu, Zeping Li, Fuwei Yang, Haiduo Huang, Jinzhang Peng, Dong Li, Lu Tian, Emad Barsoum

To address these issues, we propose a fine-grained token-wise pruning approach for the LLMs, which presents a learnable router to adaptively identify the less important tokens and skip them across model blocks to reduce computational cost during inference.

On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality

no code implementations26 Nov 2024 Jerry Yao-Chieh Hu, Weimin Wu, Yi-Chen Lee, Yu-Chao Huang, Minshuo Chen, Han Liu

We investigate the approximation and estimation rates of conditional diffusion transformers (DiTs) with classifier-free guidance.

Transformers are Deep Optimizers: Provable In-Context Learning for Deep Model Training

no code implementations25 Nov 2024 Weimin Wu, Maojiang Su, Jerry Yao-Chieh Hu, Zhao Song, Han Liu

We investigate the transformer's capability for in-context learning (ICL) to simulate the training process of deep models.

In-Context Learning

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency

no code implementations25 Nov 2024 Jerry Yao-Chieh Hu, Wei-Po Wang, Ammar Gilani, Chenyang Li, Zhao Song, Han Liu

Our key contributions are prompt tuning on \textit{single-head} transformers with only a \textit{single} self-attention layer: (i) is universal, and (ii) supports efficient (even almost-linear time) algorithms under the Strong Exponential Time Hypothesis (SETH).

On Differentially Private String Distances

no code implementations8 Nov 2024 Jerry Yao-Chieh Hu, Erzhi Liu, Han Liu, Zhao Song, Lichen Zhang

Given a database of bit strings $A_1,\ldots, A_m\in \{0, 1\}^n$, a fundamental data structure task is to estimate the distances between a given query $B\in \{0, 1\}^n$ with all the strings in the database.

Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent

3 code implementations4 Nov 2024 Xingwu Sun, Yanfeng Chen, Yiqing Huang, Ruobing Xie, Jiaqi Zhu, Kai Zhang, Shuaipeng Li, Zhen Yang, Jonny Han, Xiaobo Shu, Jiahao Bu, Zhongzhi Chen, Xuemeng Huang, Fengzong Lian, Saiyong Yang, Jianfeng Yan, Yuyuan Zeng, Xiaoqin Ren, Chao Yu, Lulu Wu, Yue Mao, Jun Xia, Tao Yang, Suncong Zheng, Kan Wu, Dian Jiao, Jinbao Xue, Xipeng Zhang, Decheng Wu, Kai Liu, Dengpeng Wu, Guanghui Xu, Shaohua Chen, Shuang Chen, Xiao Feng, Yigeng Hong, Junqiang Zheng, Chengcheng Xu, Zongwei Li, Xiong Kuang, Jianglu Hu, Yiqi Chen, Yuchi Deng, Guiyang Li, Ao Liu, Chenchen Zhang, Shihui Hu, Zilong Zhao, Zifan Wu, Yao Ding, Weichao Wang, Han Liu, Roberts Wang, Hao Fei, Peijie Yu, Ze Zhao, Xun Cao, Hai Wang, Fusheng Xiang, Mengyuan Huang, Zhiyuan Xiong, Bin Hu, Xuebin Hou, Lei Jiang, Jianqiang Ma, Jiajia Wu, Yaping Deng, Yi Shen, Qian Wang, Weijie Liu, Jie Liu, Meng Chen, Liang Dong, Weiwen Jia, Hu Chen, Feifei Liu, Rui Yuan, Huilin Xu, Zhenxiang Yan, Tengfei Cao, Zhichao Hu, Xinhua Feng, Dong Du, TingHao Yu, Yangyu Tao, Feng Zhang, Jianchen Zhu, Chengzhong Xu, Xirui Li, Chong Zha, Wen Ouyang, Yinben Xia, Xiang Li, Zekun He, Rongpeng Chen, Jiawei Song, Ruibin Chen, Fan Jiang, Chongqing Zhao, Bo wang, Hao Gong, Rong Gan, Winston Hu, Zhanhui Kang, Yong Yang, Yuhong Liu, Di Wang, Jie Jiang

In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens.

Logical Reasoning Mathematical Problem-Solving +1

Global Convergence in Training Large-Scale Transformers

no code implementations31 Oct 2024 Cheng Gao, Yuan Cao, Zihao Li, Yihan He, Mengdi Wang, Han Liu, Jason Matthew Klusowski, Jianqing Fan

First, we construct the mean-field limit of large-scale Transformers, showing that as the model width and depth go to infinity, gradient flow converges to the Wasserstein gradient flow, which is represented by a partial differential equation.

TGCA-PVT: Topic-Guided Context-Aware Pyramid Vision Transformer for Sticker Emotion Recognition

1 code implementation MM '24: Proceedings of the 32nd ACM International Conference on Multimedia 2024 Jian Chen, Wei Wang, Yuzhu Hu, Junxin Chen, Han Liu, Xiping Hu

Our approach encompasses a novel topic-guided context-aware module and a topic-guided attention mechanism, enabling the extraction of comprehensive topic context features from stickers sharing the same topic ID, significantly enhancing emotion recognition accuracy.

Emotion Recognition

Sequential LLM Framework for Fashion Recommendation

no code implementations15 Oct 2024 Han Liu, Xianfeng Tang, Tianlang Chen, Jiapeng Liu, Indu Indu, Henry Peng Zou, Peng Dai, Roberto Fernandez Galan, Michael D Porter, Dongmei Jia, Ning Zhang, Lian Xiong

The fashion industry is one of the leading domains in the global e-commerce sector, prompting major online retailers to employ recommendation systems for product suggestions and customer convenience.

Language Modeling Language Modelling +4

Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning

no code implementations9 Oct 2024 Zhengyu Hu, Yichuan Li, Zhengyu Chen, Jingang Wang, Han Liu, Kyumin Lee, Kaize Ding

Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet leveraging large language models (LLMs) for TAGs presents unique challenges due to the gap between sequential text processing and graph-structured data.

Graph Neural Network In-Context Learning +3

AdaptDiff: Cross-Modality Domain Adaptation via Weak Conditional Semantic Diffusion for Retinal Vessel Segmentation

1 code implementation6 Oct 2024 Dewei Hu, Hao Li, Han Liu, Jiacheng Wang, Xing Yao, Daiwei Lu, Ipek Oguz

Subsequently, we sample on the target domain with binary vessel masks from the source domain to get paired data, i. e., target domain synthetic images conditioned on the binary vessel map.

Image Segmentation Retinal Vessel Segmentation +3

BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models

no code implementations4 Oct 2024 Aofei Chang, Jiaqi Wang, Han Liu, Parminder Bhatia, Cao Xiao, Ting Wang, Fenglong Ma

Parameter Efficient Fine-Tuning (PEFT) offers an efficient solution for fine-tuning large pretrained language models for downstream tasks.

parameter-efficient fine-tuning

Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion?

1 code implementation2 Oct 2024 Zhenyu Pan, Rongyu Cao, Yongchang Cao, Yingwei Ma, Binhua Li, Fei Huang, Han Liu, Yongbin Li

Code completion, a key downstream task in code generation, is one of the most frequent and impactful methods for enhancing developer productivity in software development.

Code Completion Code Generation

SoK: Security and Privacy Risks of Healthcare AI

no code implementations11 Sep 2024 Yuanhaur Chang, Han Liu, Chenyang Lu, Ning Zhang

The integration of artificial intelligence (AI) and machine learning (ML) into healthcare systems holds great promise for enhancing patient care and care delivery efficiency; however, it also exposes sensitive data and system integrity to potential cyberattacks.

Influence of Early through Late Fusion on Pancreas Segmentation from Imperfectly Registered Multimodal MRI

1 code implementation6 Sep 2024 Lucas W. Remedios, Han Liu, Samuel W. Remedios, Lianrui Zuo, Adam M. Saunders, Shunxing Bao, Yuankai Huo, Alvin C. Powers, John Virostko, Bennett A. Landman

We trained a collection of basic UNets with different fusion points, spanning from early to late, to assess how early through late fusion influenced segmentation performance on imperfectly aligned images.

Image Registration Pancreas Segmentation +1

Differentially Private Kernel Density Estimation

no code implementations3 Sep 2024 Erzhi Liu, Jerry Yao-Chieh Hu, Alex Reneau, Zhao Song, Han Liu

In this paper, we improve the best previous result [Backurs, Lin, Mahabadi, Silwal, and Tarnawski, ICLR 2024] in three aspects: - We reduce query time by a factor of $\alpha^{-1} \log n$.

Density Estimation

Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Bi-parametric MRI Datasets

no code implementations8 Aug 2024 Hao Li, Han Liu, Heinrich von Busch, Robert Grimm, Henkjan Huisman, Angela Tong, David Winkel, Tobias Penzkofer, Ivan Shabunin, Moon Hyung Choi, Qingsong Yang, Dieter Szolar, Steven Shea, Fergus Coakley, Mukesh Harisinghani, Ipek Oguz, Dorin Comaniciu, Ali Kamen, Bin Lou

This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual DW images acquired using various b-values, to align with the style of images acquired using b-values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines.

Lesion Detection Unsupervised Domain Adaptation

PatchFinder: A Two-Phase Approach to Security Patch Tracing for Disclosed Vulnerabilities in Open-Source Software

no code implementations24 Jul 2024 Kaixuan Li, Jian Zhang, Sen Chen, Han Liu, Yang Liu, Yixiang Chen

In this paper, we propose PatchFinder, a two-phase framework with end-to-end correlation learning for better-tracing security patches.

Re-Ranking

On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)

no code implementations1 Jul 2024 Jerry Yao-Chieh Hu, Weimin Wu, Zhao Song, Han Liu

For backward computation, we leverage the low-rank structure within the gradient computation of DiTs training for possible algorithmic speedup.

Computational Efficiency

Predicting fluorescent labels in label-free microscopy images with pix2pix and adaptive loss in Light My Cells challenge

1 code implementation22 Jun 2024 Han Liu, Hao Li, Jiacheng Wang, Yubo Fan, Zhoubing Xu, Ipek Oguz

Recently, in silico labeling has emerged as a promising alternative, aiming to use machine learning models to directly predict the fluorescently labeled images from label-free microscopy.

Partially Labeled Datasets

Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods

no code implementations20 Jun 2024 Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu, Han Liu, Mladen Kolar

Despite an understanding of their convergence and the importance of batch sizes for training efficiency and generalization, optimal batch sizes for local gradient methods are difficult to determine.

image-classification Image Classification +2

Full-ECE: A Metric For Token-level Calibration on Large Language Models

no code implementations17 Jun 2024 Han Liu, Yupeng Zhang, Bingning Wang, WeiPeng Chen, Xiaolin Hu

Deep Neural Networks (DNNs) excel in various domains but face challenges in providing accurate uncertainty estimates, which are crucial for high-stakes applications.

Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models

no code implementations5 Jun 2024 Jerry Yao-Chieh Hu, Maojiang Su, En-Jui Kuo, Zhao Song, Han Liu

We study the computational limits of Low-Rank Adaptation (LoRA) for finetuning transformer-based models using fine-grained complexity theory.

Decoupled Alignment for Robust Plug-and-Play Adaptation

no code implementations3 Jun 2024 Haozheng Luo, Jiahao Yu, Wenxin Zhang, Jialong Li, Jerry Yao-Chieh Hu, Xinyu Xing, Han Liu

We introduce a low-resource safety enhancement method for aligning large language models (LLMs) without the need for supervised fine-tuning (SFT) or reinforcement learning from human feedback (RLHF).

Knowledge Distillation

Enhancing Jailbreak Attack Against Large Language Models through Silent Tokens

no code implementations31 May 2024 Jiahao Yu, Haozheng Luo, Jerry Yao-Chieh Hu, Wenbo Guo, Han Liu, Xinyu Xing

Attackers carefully craft jailbreaking prompts such that a target LLM will respond to the harmful question.

Safety Alignment

Accurate and Reliable Predictions with Mutual-Transport Ensemble

no code implementations30 May 2024 Han Liu, Peng Cui, Bingning Wang, Jun Zhu, Xiaolin Hu

Deep Neural Networks (DNNs) have achieved remarkable success in a variety of tasks, especially when it comes to prediction accuracy.

Prediction

Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action

no code implementations28 May 2024 Zhenyu Pan, Haozheng Luo, Manling Li, Han Liu

Methodologically, we propose a resource-efficiency Hopfield retriever to enhance the efficiency and accuracy of conversational information retrieval within our actions.

Conversational Question Answering Hallucination +3

HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model

no code implementations22 May 2024 Zhenyu Pan, Yoonsung Jeong, Xiaoda Liu, Han Liu

We propose a heterogeneous graph mamba network (HGMN) as the first exploration in leveraging the selective state space models (SSSMs) for heterogeneous graph learning.

Graph Learning Mamba +1

Input Snapshots Fusion for Scalable Discrete Dynamic Graph Nerual Networks

no code implementations11 May 2024 QingGuo Qi, Hongyang Chen, Minhao Cheng, Han Liu

Additionally, for link prediction tasks on discrete dynamic graphs, the requirement of substantial GPU memory to store embeddings of all nodes hinders the scalability of existing models.

Denoising Link Prediction

MMGRec: Multimodal Generative Recommendation with Transformer Model

no code implementations25 Apr 2024 Han Liu, Yinwei Wei, Xuemeng Song, Weili Guan, Yuan-Fang Li, Liqiang Nie

Multimodal recommendation aims to recommend user-preferred candidates based on her/his historically interacted items and associated multimodal information.

model Multimodal Recommendation +2

EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM

no code implementations13 Apr 2024 Henry Peng Zou, Gavin Heqing Yu, Ziwei Fan, Dan Bu, Han Liu, Peng Dai, Dongmei Jia, Cornelia Caragea

To address these issues, we introduce EIVEN, a data- and parameter-efficient generative framework that pioneers the use of multimodal LLM for implicit attribute value extraction.

Attribute Attribute Value Extraction +1

Nonparametric Modern Hopfield Models

1 code implementation5 Apr 2024 Jerry Yao-Chieh Hu, Bo-Yu Chen, Dennis Wu, Feng Ruan, Han Liu

We present a nonparametric construction for deep learning compatible modern Hopfield models and utilize this framework to debut an efficient variant.

Outlier-Efficient Hopfield Layers for Large Transformer-Based Models

1 code implementation4 Apr 2024 Jerry Yao-Chieh Hu, Pei-Hsuan Chang, Robin Luo, Hong-Yu Chen, Weijian Li, Wei-Po Wang, Han Liu

We introduce an Outlier-Efficient Modern Hopfield Model (termed $\mathrm{OutEffHop}$) and use it to address the outlier inefficiency problem of {training} gigantic transformer-based models.

Benchmarking Quantization +1

Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models

1 code implementation4 Apr 2024 Dennis Wu, Jerry Yao-Chieh Hu, Teng-Yun Hsiao, Han Liu

Specifically, we accomplish this by constructing a separation loss $\mathcal{L}_\Phi$ that separates the local minima of kernelized energy by separating stored memory patterns in kernel space.

Retrieval

BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model

1 code implementation4 Apr 2024 Chenwei Xu, Yu-Chao Huang, Jerry Yao-Chieh Hu, Weijian Li, Ammar Gilani, Hsi-Sheng Goan, Han Liu

We introduce the \textbf{B}i-Directional \textbf{S}parse \textbf{Hop}field Network (\textbf{BiSHop}), a novel end-to-end framework for deep tabular learning.

Representation Learning

Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models

1 code implementation26 Mar 2024 Zhenyu Pan, Haozheng Luo, Manling Li, Han Liu

We present a Chain-of-Action (CoA) framework for multimodal and retrieval-augmented Question-Answering (QA).

 Ranked #1 on Question Answering on TruthfulQA (EM metric)

Hallucination Information Retrieval +2

USE: Dynamic User Modeling with Stateful Sequence Models

1 code implementation20 Mar 2024 Zhihan Zhou, Qixiang Fang, Leonardo Neves, Francesco Barbieri, Yozen Liu, Han Liu, Maarten W. Bos, Ron Dotsch

Furthermore, we introduce a novel training objective named future W-behavior prediction to transcend the limitations of next-token prediction by forecasting a broader horizon of upcoming user behaviors.

Contrastive Learning

On Languaging a Simulation Engine

no code implementations26 Feb 2024 Han Liu, Liantang Li

Language model intelligence is revolutionizing the way we program materials simulations.

Diversity Language Modeling +1

AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods

no code implementations17 Feb 2024 Tim Tsz-Kit Lau, Han Liu, Mladen Kolar

The choice of batch sizes in minibatch stochastic gradient optimizers is critical in large-scale model training for both optimization and generalization performance.

image-classification Image Classification

VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models

no code implementations16 Feb 2024 Ziyi Yin, Muchao Ye, Tianrong Zhang, Jiaqi Wang, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma

Correspondingly, we propose a novel VQAttack model, which can iteratively generate both image and text perturbations with the designed modules: the large language model (LLM)-enhanced image attack and the cross-modal joint attack module.

Adversarial Robustness Language Modelling +3

DNABERT-S: Pioneering Species Differentiation with Species-Aware DNA Embeddings

2 code implementations13 Feb 2024 Zhihan Zhou, Weimin Wu, Harrison Ho, Jiayi Wang, Lizhen Shi, Ramana V Davuluri, Zhong Wang, Han Liu

We introduce DNABERT-S, a tailored genome model that develops species-aware embeddings to naturally cluster and segregate DNA sequences of different species in the embedding space.

Contrastive Learning

On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis

no code implementations7 Feb 2024 Jerry Yao-Chieh Hu, Thomas Lin, Zhao Song, Han Liu

Specifically, we establish an upper bound criterion for the norm of input query patterns and memory patterns.

Retrieval

HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text

1 code implementation NeurIPS 2023 Han Liu, Zhi Xu, Xiaotong Zhang, Feng Zhang, Fenglong Ma, Hongyang Chen, Hong Yu, Xianchao Zhang

Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted label is accessible.

Adversarial Attack Hard-label Attack +5

LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning

no code implementations29 Jan 2024 Yuqiang Sun, Daoyuan Wu, Yue Xue, Han Liu, Wei Ma, Lyuye Zhang, Yang Liu, Yingjiu Li

In this paper, we aim to decouple LLMs' vulnerability reasoning from other capabilities, such as vulnerability knowledge adoption, context information retrieval, and advanced prompt schemes.

Information Retrieval Retrieval +2

Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions

1 code implementation20 Jan 2024 Suhan Cui, Jiaqi Wang, Yuan Zhong, Han Liu, Ting Wang, Fenglong Ma

The widespread adoption of Electronic Health Record (EHR) systems in healthcare institutes has generated vast amounts of medical data, offering significant opportunities for improving healthcare services through deep learning techniques.

Deep Learning Neural Architecture Search

STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction

1 code implementation28 Dec 2023 Dennis Wu, Jerry Yao-Chieh Hu, Weijian Li, Bo-Yu Chen, Han Liu

We present STanHop-Net (Sparse Tandem Hopfield Network) for multivariate time series prediction with memory-enhanced capabilities.

Retrieval Time Series +1

Sparse PCA with Oracle Property

no code implementations NeurIPS 2014 Quanquan Gu, Zhaoran Wang, Han Liu

In particular, under a weak assumption on the magnitude of the population projection matrix, one estimator within this family exactly recovers the true support with high probability, has exact rank-$k$, and attains a $\sqrt{s/n}$ statistical rate of convergence with $s$ being the subspace sparsity level and $n$ the sample size.

Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptation

1 code implementation21 Nov 2023 Han Liu, Yubo Fan, Zhoubing Xu, Benoit M. Dawant, Ipek Oguz

In this paper, we present our solution to tackle the multi-institutional unsupervised domain adaptation for the crossMoDA 2023 challenge.

Medical Image Analysis Medical Image Segmentation +2

Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models

1 code implementation30 Oct 2023 Hao Li, Han Liu, Dewei Hu, Jiacheng Wang, Ipek Oguz

To address prevalent issues in medical imaging, such as data acquisition challenges and label availability, transfer learning from natural to medical image domains serves as a viable strategy to produce reliable segmentation results.

Image Segmentation Medical Image Segmentation +4

Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors

no code implementations29 Oct 2023 Han Liu, Xingshuo Huang, Xiaotong Zhang, Qimai Li, Fenglong Ma, Wei Wang, Hongyang Chen, Hong Yu, Xianchao Zhang

Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction.

Adversarial Attack

VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models

1 code implementation NeurIPS 2023 Ziyi Yin, Muchao Ye, Tianrong Zhang, Tianyu Du, Jinguo Zhu, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma

In this paper, we aim to investigate a new yet practical task to craft image and text perturbations using pre-trained VL models to attack black-box fine-tuned models on different downstream tasks.

Adversarial Robustness

Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints

1 code implementation28 Sep 2023 Chaoqi Wang, Yibo Jiang, Chenghao Yang, Han Liu, Yuxin Chen

The increasing capabilities of large language models (LLMs) raise opportunities for artificial general intelligence but concurrently amplify safety concerns, such as potential misuse of AI systems, necessitating effective AI alignment.

On Sparse Modern Hopfield Model

1 code implementation NeurIPS 2023 Jerry Yao-Chieh Hu, Donglin Yang, Dennis Wu, Chenwei Xu, Bo-Yu Chen, Han Liu

Building upon this, we derive the sparse memory retrieval dynamics from the sparse energy function and show its one-step approximation is equivalent to the sparse-structured attention.

model Retrieval

False Negative/Positive Control for SAM on Noisy Medical Images

1 code implementation20 Aug 2023 Xing Yao, Han Liu, Dewei Hu, Daiwei Lu, Ange Lou, Hao Li, Ruining Deng, Gabriel Arenas, Baris Oguz, Nadav Schwartz, Brett C Byram, Ipek Oguz

The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy.

Image Segmentation Medical Image Segmentation +2

CATS v2: Hybrid encoders for robust medical segmentation

2 code implementations11 Aug 2023 Hao Li, Han Liu, Dewei Hu, Xing Yao, Jiacheng Wang, Ipek Oguz

We fuse the information from the convolutional encoder and the transformer at the skip connections of different resolutions to form the final segmentation.

Domain Adaptation Image Segmentation +3

GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis

1 code implementation7 Aug 2023 Yuqiang Sun, Daoyuan Wu, Yue Xue, Han Liu, Haijun Wang, Zhengzi Xu, Xiaofei Xie, Yang Liu

Instead of relying solely on GPT to identify vulnerabilities, which can lead to high false positives and is limited by GPT's pre-trained knowledge, we utilize GPT as a versatile code understanding tool.

Vulnerability Detection

COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation

1 code implementation22 Jul 2023 Han Liu, Hao Li, Xing Yao, Yubo Fan, Dewei Hu, Benoit Dawant, Vishwesh Nath, Zhoubing Xu, Ipek Oguz

Cold-start AL is highly relevant in many practical scenarios but has been under-explored, especially for 3D medical segmentation tasks requiring substantial annotation effort.

Active Learning Image Segmentation +4

Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty

no code implementations15 Jul 2023 Guanlin Liu, Zhihan Zhou, Han Liu, Lifeng Lai

Robust reinforcement learning (RL) aims to find a policy that optimizes the worst-case performance in the face of uncertainties.

reinforcement-learning Reinforcement Learning +1

Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints

no code implementations13 Jul 2023 Jaeyeon Jang, Diego Klabjan, Han Liu, Nital S. Patel, Xiuqi Li, Balakrishnan Ananthanarayanan, Husam Dauod, Tzung-Han Juang

This paper proposes a novel multi-agent reinforcement learning (MARL) method to learn multiple coordinated agents under directed acyclic graph (DAG) constraints.

Multi-agent Reinforcement Learning Scheduling

Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation

1 code implementation1 Jul 2023 Yongshang Li, Ronggui Ma, Han Liu, Gaoli Cheng

Deep learning plays an important role in crack segmentation, but most work utilize off-the-shelf or improved models that have not been specifically developed for this task.

Crack Segmentation Segmentation

DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome

6 code implementations26 Jun 2023 Zhihan Zhou, Yanrong Ji, Weijian Li, Pratik Dutta, Ramana Davuluri, Han Liu

Decoding the linguistic intricacies of the genome is a crucial problem in biology, and pre-trained foundational models such as DNABERT and Nucleotide Transformer have made significant strides in this area.

Computational Efficiency Core Promoter Detection +9

Feature Programming for Multivariate Time Series Prediction

1 code implementation9 Jun 2023 Alex Reneau, Jerry Yao-Chieh Hu, Chenwei Xu, Weijian Li, Ammar Gilani, Han Liu

We introduce the concept of programmable feature engineering for time series modeling and propose a feature programming framework.

Automated Feature Engineering Feature Engineering +4

Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms

1 code implementation25 May 2023 Tim Tsz-Kit Lau, Han Liu, Thomas Pock

We study the problem of approximate sampling from non-log-concave distributions, e. g., Gaussian mixtures, which is often challenging even in low dimensions due to their multimodality.

Bayesian Inference Image Deconvolution

Boosting Few-Shot Text Classification via Distribution Estimation

no code implementations26 Mar 2023 Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao, Fenglong Ma, Xiao-Ming Wu, Hongyang Chen, Hong Yu, Xianchao Zhang

Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain.

Few-Shot Image Classification Few-Shot Text Classification +2

Learning Human-Compatible Representations for Case-Based Decision Support

1 code implementation6 Mar 2023 Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, Chenhao Tan

Despite the promising performance of supervised learning, representations learned by supervised models may not align well with human intuitions: what models consider as similar examples can be perceived as distinct by humans.

Classification Decision Making +2

Real-Time Image Demoireing on Mobile Devices

1 code implementation4 Feb 2023 Yuxin Zhang, Mingbao Lin, Xunchao Li, Han Liu, Guozhi Wang, Fei Chao, Shuai Ren, Yafei Wen, Xiaoxin Chen, Rongrong Ji

In this paper, we launch the first study on accelerating demoireing networks and propose a dynamic demoireing acceleration method (DDA) towards a real-time deployment on mobile devices.

HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation

1 code implementation13 Jan 2023 Han Liu, Yinwei Wei, Jianhua Yin, Liqiang Nie

Towards this end, existing methods tend to code users by modeling their Hamming similarities with the items they historically interact with, which are termed as the first-order similarities in this work.

Recommendation Systems

SlowLiDAR: Increasing the Latency of LiDAR-Based Detection Using Adversarial Examples

1 code implementation CVPR 2023 Han Liu, Yuhao Wu, Zhiyuan Yu, Yevgeniy Vorobeychik, Ning Zhang

LiDAR-based perception is a central component of autonomous driving, playing a key role in tasks such as vehicle localization and obstacle detection.

Autonomous Driving

Biomedical image analysis competitions: The state of current participation practice

no code implementations16 Dec 2022 Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Patrick Godau, Veronika Cheplygina, Michal Kozubek, Sharib Ali, Anubha Gupta, Jan Kybic, Alison Noble, Carlos Ortiz de Solórzano, Samiksha Pachade, Caroline Petitjean, Daniel Sage, Donglai Wei, Elizabeth Wilden, Deepak Alapatt, Vincent Andrearczyk, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Vivek Singh Bawa, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Jinwook Choi, Olivier Commowick, Marie Daum, Adrien Depeursinge, Reuben Dorent, Jan Egger, Hannah Eichhorn, Sandy Engelhardt, Melanie Ganz, Gabriel Girard, Lasse Hansen, Mattias Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Hyunjeong Kim, Bennett Landman, Hongwei Bran Li, Jianning Li, Jun Ma, Anne Martel, Carlos Martín-Isla, Bjoern Menze, Chinedu Innocent Nwoye, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Carole Sudre, Kimberlin Van Wijnen, Armine Vardazaryan, Tom Vercauteren, Martin Wagner, Chuanbo Wang, Moi Hoon Yap, Zeyun Yu, Chun Yuan, Maximilian Zenk, Aneeq Zia, David Zimmerer, Rina Bao, Chanyeol Choi, Andrew Cohen, Oleh Dzyubachyk, Adrian Galdran, Tianyuan Gan, Tianqi Guo, Pradyumna Gupta, Mahmood Haithami, Edward Ho, Ikbeom Jang, Zhili Li, Zhengbo Luo, Filip Lux, Sokratis Makrogiannis, Dominik Müller, Young-tack Oh, Subeen Pang, Constantin Pape, Gorkem Polat, Charlotte Rosalie Reed, Kanghyun Ryu, Tim Scherr, Vajira Thambawita, Haoyu Wang, Xinliang Wang, Kele Xu, Hung Yeh, Doyeob Yeo, Yixuan Yuan, Yan Zeng, Xin Zhao, Julian Abbing, Jannes Adam, Nagesh Adluru, Niklas Agethen, Salman Ahmed, Yasmina Al Khalil, Mireia Alenyà, Esa Alhoniemi, Chengyang An, Talha Anwar, Tewodros Weldebirhan Arega, Netanell Avisdris, Dogu Baran Aydogan, Yingbin Bai, Maria Baldeon Calisto, Berke Doga Basaran, Marcel Beetz, Cheng Bian, Hao Bian, Kevin Blansit, Louise Bloch, Robert Bohnsack, Sara Bosticardo, Jack Breen, Mikael Brudfors, Raphael Brüngel, Mariano Cabezas, Alberto Cacciola, Zhiwei Chen, Yucong Chen, Daniel Tianming Chen, Minjeong Cho, Min-Kook Choi, Chuantao Xie Chuantao Xie, Dana Cobzas, Julien Cohen-Adad, Jorge Corral Acero, Sujit Kumar Das, Marcela de Oliveira, Hanqiu Deng, Guiming Dong, Lars Doorenbos, Cory Efird, Sergio Escalera, Di Fan, Mehdi Fatan Serj, Alexandre Fenneteau, Lucas Fidon, Patryk Filipiak, René Finzel, Nuno R. Freitas, Christoph M. Friedrich, Mitchell Fulton, Finn Gaida, Francesco Galati, Christoforos Galazis, Chang Hee Gan, Zheyao Gao, Shengbo Gao, Matej Gazda, Beerend Gerats, Neil Getty, Adam Gibicar, Ryan Gifford, Sajan Gohil, Maria Grammatikopoulou, Daniel Grzech, Orhun Güley, Timo Günnemann, Chunxu Guo, Sylvain Guy, Heonjin Ha, Luyi Han, Il Song Han, Ali Hatamizadeh, Tian He, Jimin Heo, Sebastian Hitziger, SeulGi Hong, Seungbum Hong, Rian Huang, Ziyan Huang, Markus Huellebrand, Stephan Huschauer, Mustaffa Hussain, Tomoo Inubushi, Ece Isik Polat, Mojtaba Jafaritadi, SeongHun Jeong, Bailiang Jian, Yuanhong Jiang, Zhifan Jiang, Yueming Jin, Smriti Joshi, Abdolrahim Kadkhodamohammadi, Reda Abdellah Kamraoui, Inha Kang, Junghwa Kang, Davood Karimi, April Khademi, Muhammad Irfan Khan, Suleiman A. Khan, Rishab Khantwal, Kwang-Ju Kim, Timothy Kline, Satoshi Kondo, Elina Kontio, Adrian Krenzer, Artem Kroviakov, Hugo Kuijf, Satyadwyoom Kumar, Francesco La Rosa, Abhi Lad, Doohee Lee, Minho Lee, Chiara Lena, Hao Li, Ling Li, Xingyu Li, Fuyuan Liao, Kuanlun Liao, Arlindo Limede Oliveira, Chaonan Lin, Shan Lin, Akis Linardos, Marius George Linguraru, Han Liu, Tao Liu, Di Liu, Yanling Liu, João Lourenço-Silva, Jingpei Lu, Jiangshan Lu, Imanol Luengo, Christina B. Lund, Huan Minh Luu, Yi Lv, Uzay Macar, Leon Maechler, Sina Mansour L., Kenji Marshall, Moona Mazher, Richard McKinley, Alfonso Medela, Felix Meissen, Mingyuan Meng, Dylan Miller, Seyed Hossein Mirjahanmardi, Arnab Mishra, Samir Mitha, Hassan Mohy-ud-Din, Tony Chi Wing Mok, Gowtham Krishnan Murugesan, Enamundram Naga Karthik, Sahil Nalawade, Jakub Nalepa, Mohamed Naser, Ramin Nateghi, Hammad Naveed, Quang-Minh Nguyen, Cuong Nguyen Quoc, Brennan Nichyporuk, Bruno Oliveira, David Owen, Jimut Bahan Pal, Junwen Pan, Wentao Pan, Winnie Pang, Bogyu Park, Vivek Pawar, Kamlesh Pawar, Michael Peven, Lena Philipp, Tomasz Pieciak, Szymon Plotka, Marcel Plutat, Fattaneh Pourakpour, Domen Preložnik, Kumaradevan Punithakumar, Abdul Qayyum, Sandro Queirós, Arman Rahmim, Salar Razavi, Jintao Ren, Mina Rezaei, Jonathan Adam Rico, ZunHyan Rieu, Markus Rink, Johannes Roth, Yusely Ruiz-Gonzalez, Numan Saeed, Anindo Saha, Mostafa Salem, Ricardo Sanchez-Matilla, Kurt Schilling, Wei Shao, Zhiqiang Shen, Ruize Shi, Pengcheng Shi, Daniel Sobotka, Théodore Soulier, Bella Specktor Fadida, Danail Stoyanov, Timothy Sum Hon Mun, Xiaowu Sun, Rong Tao, Franz Thaler, Antoine Théberge, Felix Thielke, Helena Torres, Kareem A. Wahid, Jiacheng Wang, Yifei Wang, Wei Wang, Xiong Wang, Jianhui Wen, Ning Wen, Marek Wodzinski, Ye Wu, Fangfang Xia, Tianqi Xiang, Chen Xiaofei, Lizhan Xu, Tingting Xue, Yuxuan Yang, Lin Yang, Kai Yao, Huifeng Yao, Amirsaeed Yazdani, Michael Yip, Hwanseung Yoo, Fereshteh Yousefirizi, Shunkai Yu, Lei Yu, Jonathan Zamora, Ramy Ashraf Zeineldin, Dewen Zeng, Jianpeng Zhang, Bokai Zhang, Jiapeng Zhang, Fan Zhang, Huahong Zhang, Zhongchen Zhao, Zixuan Zhao, Jiachen Zhao, Can Zhao, Qingshuo Zheng, Yuheng Zhi, Ziqi Zhou, Baosheng Zou, Klaus Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein

Of these, 84% were based on standard architectures.

Benchmarking Survey

KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description

no code implementations30 Nov 2022 Chunyu Ma, Zhihan Zhou, Han Liu, David Koslicki

We believe it can effectively reduce "black-box" concerns and increase prediction confidence for drug repurposing based on predicted path-based explanations, and further accelerate the process of drug discovery for emerging diseases.

Drug Discovery

Drug repositioning for Alzheimer's disease with transfer learning

no code implementations27 Oct 2022 Yetao Wu, Han Liu, Jie Yan, Xiaolin Hu

After training, the model is used for virtual screening to find potential drugs for Alzheimer's disease (AD) treatment.

Deep Learning Drug Discovery +1

Evaluation of Synthetically Generated CT for use in Transcranial Focused Ultrasound Procedures

1 code implementation26 Oct 2022 Han Liu, Michelle K. Sigona, Thomas J. Manuel, Li Min Chen, Benoit M. Dawant, Charles F. Caskey

Among 20 targets, differences in simulated peak pressure between rCT and sCT were largest without phase correction (12. 4$\pm$8. 1%) and smallest with Kranion phases (7. 3$\pm$6. 0%).

Generative Adversarial Network

Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation

1 code implementation16 Oct 2022 Ho Hin Lee, Yucheng Tang, Han Liu, Yubo Fan, Leon Y. Cai, Qi Yang, Xin Yu, Shunxing Bao, Yuankai Huo, Bennett A. Landman

We evaluate our proposed approach on multi-organ segmentation with both non-contrast CT (NCCT) datasets and the MICCAI 2015 BTCV Challenge contrast-enhance CT (CECT) datasets.

Computed Tomography (CT) Contrastive Learning +1

Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation

no code implementations23 Sep 2022 Han Liu, Yubo Fan, Ipek Oguz, Benoit M. Dawant

Automatic segmentation of vestibular schwannoma (VS) and cochlea from magnetic resonance imaging can facilitate VS treatment planning.

Diversity Segmentation +2

Cats: Complementary CNN and Transformer Encoders for Segmentation

no code implementations24 Aug 2022 Hao Li, Dewei Hu, Han Liu, Jiacheng Wang, Ipek Oguz

We fuse the information from the convolutional encoder and the transformer, and pass it to the decoder to obtain the results.

3D Medical Imaging Segmentation Decoder +2

A Real-time Fire Segmentation Method Based on A Deep Learning Approach

1 code implementation IFAC-PapersOnLine 2022 Mengna Li, Youmin Zhang, Lingxia Mu, Jing Xin, Ziquan Yu, Shangbin Jiao, Han Liu, Guo Xie, Yi Yingmin

Different from deeplabv3+, in order to improve the segmentation speed, this paper uses the lightweight network mobilenetv3 to build a new deep convolutional neural network and does not use atrous convolution, but it will affect the segmentation accuracy.

Decoder Real-Time Semantic Segmentation +1

Bregman Proximal Langevin Monte Carlo via Bregman--Moreau Envelopes

1 code implementation10 Jul 2022 Tim Tsz-Kit Lau, Han Liu

The proposed algorithms extend existing Langevin Monte Carlo algorithms in two aspects -- the ability to sample nonsmooth distributions with mirror descent-like algorithms, and the use of the more general Bregman--Moreau envelope in place of the Moreau envelope as a smooth approximation of the nonsmooth part of the potential.

Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection

no code implementations14 Jun 2022 Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao, Junjie Sun, Hong Yu, Xianchao Zhang

Multi-label aspect category detection allows a given review sentence to contain multiple aspect categories, which is shown to be more practical in sentiment analysis and attracting increasing attention.

Aspect Category Detection Contrastive Learning +2

A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism

no code implementations5 Jun 2022 Han Liu, Siyang Zhao, Xiaotong Zhang, Feng Zhang, Junjie Sun, Hong Yu, Xianchao Zhang

Zero-shot intent classification is a vital and challenging task in dialogue systems, which aims to deal with numerous fast-emerging unacquainted intents without annotated training data.

Classification intent-classification +4

Wasserstein Distributionally Robust Optimization with Wasserstein Barycenters

no code implementations23 Mar 2022 Tim Tsz-Kit Lau, Han Liu

On the other hand, in distributionally robust optimization, we seek data-driven decisions which perform well under the most adverse distribution from a nominal distribution constructed from data samples within a certain discrepancy of probability distributions.

Learning to Infer Belief Embedded Communication

no code implementations15 Mar 2022 Guo Ye, Han Liu, Biswa Sengupta

In multi-agent collaboration problems with communication, an agent's ability to encode their intention and interpret other agents' strategies is critical for planning their future actions.

Text Generation

Switch Trajectory Transformer with Distributional Value Approximation for Multi-Task Reinforcement Learning

no code implementations14 Mar 2022 Qinjie Lin, Han Liu, Biswa Sengupta

Our results also demonstrate the advantage of the switch transformer model for absorbing expert knowledge and the importance of value distribution in evaluating the trajectory.

reinforcement-learning Reinforcement Learning (RL)

Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomics, and Demographic Data

no code implementations8 Mar 2022 Can Cui, Han Liu, Quan Liu, Ruining Deng, Zuhayr Asad, Yaohong WangShilin Zhao, Haichun Yang, Bennett A. Landman, Yuankai Huo

Thus, there are still open questions on how to effectively predict brain cancer survival from the incomplete radiological, pathological, genomic, and demographic data (e. g., one or more modalities might not be collected for a patient).

Computational Efficiency Prediction +1

ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities

1 code implementation7 Mar 2022 Han Liu, Yubo Fan, Hao Li, Jiacheng Wang, Dewei Hu, Can Cui, Ho Hin Lee, Huahong Zhang, Ipek Oguz

Previously, a training strategy termed Modality Dropout (ModDrop) has been applied to MS lesion segmentation to achieve the state-of-the-art performance with missing modality.

Lesion Segmentation

Modeling and Validating Temporal Rules with Semantic Petri-Net for Digital Twins

no code implementations4 Mar 2022 Han Liu, Xiaoyu Song, Ge Gao, Hehua Zhang, Yu-Shen Liu, Ming Gu

Semantic rule checking on RDFS/OWL data has been widely used in the construction industry.

Synthetic CT Skull Generation for Transcranial MR Imaging-Guided Focused Ultrasound Interventions with Conditional Adversarial Networks

1 code implementation21 Feb 2022 Han Liu, Michelle K. Sigona, Thomas J. Manuel, Li Min Chen, Charles F. Caskey, Benoit M. Dawant

Transcranial MRI-guided focused ultrasound (TcMRgFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively under MRI guidance.

Generative Adversarial Network

Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion

no code implementations25 Jan 2022 Han Liu, Yubo Fan, Can Cui, Dingjie Su, Andrew McNeil, Benoit M. Dawant

Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning.

Segmentation Unsupervised Domain Adaptation

A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications

no code implementations3 Nov 2021 Xinlei Zhou, Han Liu, Farhad Pourpanah, Tieyong Zeng, XiZhao Wang

This paper provides a comprehensive review of epistemic uncertainty learning techniques in supervised learning over the last five years.

Learning Predictive, Online Approximations of Explanatory, Offline Algorithms

no code implementations29 Sep 2021 Mattson Thieme, Ammar Gilani, Han Liu

In this work, we introduce a general methodology for approximating offline algorithms in online settings.

Multi-Task Learning

Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory

no code implementations ICLR 2022 Zhi Zhang, Zhuoran Yang, Han Liu, Pratap Tokekar, Furong Huang

This paper proposes a new algorithm for learning the optimal policies under a novel multi-agent predictive state representation reinforcement learning model.

reinforcement-learning Reinforcement Learning (RL)

Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation

no code implementations13 Sep 2021 Han Liu, Yubo Fan, Can Cui, Dingjie Su, Andrew McNeil, Benoit M. Dawant

Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning.

Segmentation Unsupervised Domain Adaptation

Posterior Promoted GAN With Distribution Discriminator for Unsupervised Image Synthesis

no code implementations CVPR 2021 Xianchao Zhang, Ziyang Cheng, Xiaotong Zhang, Han Liu

In this paper, we propose a novel variant of GAN, Posterior Promoted GAN (P2GAN), which promotes generator with the real information in the posterior distribution produced by discriminator.

Image Generation

Review Polarity-wise Recommender

1 code implementation8 Jun 2021 Han Liu, Yangyang Guo, Jianhua Yin, Zan Gao, Liqiang Nie

To be specific, in this model, positive and negative reviews are separately gathered and utilized to model the user-preferred and user-rejected aspects, respectively.

Recommendation Systems

Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading

1 code implementation Findings (ACL) 2021 Zhihan Zhou, Liqian Ma, Han Liu

In this paper, we introduce an event-driven trading strategy that predicts stock movements by detecting corporate events from news articles.

Articles Event Detection +3

Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving

no code implementations21 Mar 2021 Li Wang, Dong Li, Han Liu, Jinzhang Peng, Lu Tian, Yi Shan

Our goal is to train a unified model for improving the performance in each dataset by leveraging information from all the datasets.

3D Semantic Segmentation Autonomous Driving +3

BLOCKEYE: Hunting For DeFi Attacks on Blockchain

no code implementations4 Mar 2021 Bin Wang, Han Liu, Chao Liu, Zhiqiang Yang, Qian Ren, Huixuan Zheng, Hong Lei

We applied BLOCKEYE in several popular DeFi projects and managed to discover potential security attacks that are unreported before.

Cryptography and Security Computers and Society

Converse, Focus and Guess -- Towards Multi-Document Driven Dialogue

1 code implementation4 Feb 2021 Han Liu, Caixia Yuan, Xiaojie Wang, Yushu Yang, Huixing Jiang, Zhongyuan Wang

We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue.

Attribute

Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making

no code implementations13 Jan 2021 Han Liu, Vivian Lai, Chenhao Tan

Although AI holds promise for improving human decision making in societally critical domains, it remains an open question how human-AI teams can reliably outperform AI alone and human alone in challenging prediction tasks (also known as complementary performance).

Decision Making Open-Ended Question Answering

Morphology Matters: A Multilingual Language Modeling Analysis

1 code implementation11 Dec 2020 Hyunji Hayley Park, Katherine J. Zhang, Coleman Haley, Kenneth Steimel, Han Liu, Lane Schwartz

We fill in missing typological data for several languages and consider corpus-based measures of morphological complexity in addition to expert-produced typological features.

Language Modeling Language Modelling +1

Uncertainty Estimation in Medical Image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation

no code implementations3 Nov 2020 Han Liu, Can Cui, Dario J. Englot, Benoit M. Dawant

Atlas-based methods are the standard approaches for automatic targeting of the Anterior Nucleus of the Thalamus (ANT) for Deep Brain Stimulation (DBS), but these are known to lack robustness when anatomic differences between atlases and subjects are large.

Label-Wise Document Pre-Training for Multi-Label Text Classification

1 code implementation15 Aug 2020 Han Liu, Caixia Yuan, Xiaojie Wang

A major challenge of multi-label text classification (MLTC) is to stimulatingly exploit possible label differences and label correlations.

 Ranked #1 on Multi-Label Text Classification on AAPD (Micro F1 metric)

Document Classification General Classification +3

Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python

1 code implementation27 Jun 2020 Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, Tuo Zhao

We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e. g., sparse linear regression, sparse logistic regression, sparse Poisson regression and scaled sparse linear regression) combined with efficient active set selection strategies.

regression Sparse Learning

The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R

no code implementations27 Jun 2020 Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu

This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, $\ell_q$ Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME).

regression

The huge Package for High-dimensional Undirected Graph Estimation in R

no code implementations26 Jun 2020 Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman

We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data.

Model Selection Vocal Bursts Intensity Prediction

A Deep Learning based Wearable Healthcare IoT Device for AI-enabled Hearing Assistance Automation

no code implementations16 May 2020 Fraser Young, L. Zhang, Richard Jiang, Han Liu, Conor Wall

With the recent booming of artificial intelligence (AI), particularly deep learning techniques, digital healthcare is one of the prevalent areas that could gain benefits from AI-enabled functionality.

speech-recognition Speech Recognition +1

FAME: 3D Shape Generation via Functionality-Aware Model Evolution

1 code implementation9 May 2020 Yanran Guan, Han Liu, Kun Liu, Kangxue Yin, Ruizhen Hu, Oliver van Kaick, Yan Zhang, Ersin Yumer, Nathan Carr, Radomir Mech, Hao Zhang

Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels.

Graphics

EQL -- an extremely easy to learn knowledge graph query language, achieving highspeed and precise search

no code implementations19 Mar 2020 Han Liu, Shantao Liu

EQL, also named as Extremely Simple Query Language, can be widely used in the field of knowledge graph, precise search, strong artificial intelligence, database, smart speaker , patent search and other fields.

"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans

no code implementations14 Jan 2020 Vivian Lai, Han Liu, Chenhao Tan

To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans.

Decision Making

Automatic quality assessment for 2D fetal sonographic standard plane based on multi-task learning

no code implementations11 Dec 2019 Hong Luo, Han Liu, Kejun Li, Bo Zhang

An essential criterion for FS image quality control is that all the essential anatomical structures in the section should appear full and remarkable with a clear boundary.

Image Quality Assessment Multi-Task Learning +1

Reconstructing Capsule Networks for Zero-shot Intent Classification

1 code implementation IJCNLP 2019 Han Liu, Xiaotong Zhang, Lu Fan, Xu Fu, i, Qimai Li, Xiao-Ming Wu, Albert Y. S. Lam

With the burgeoning of conversational AI, existing systems are not capable of handling numerous fast-emerging intents, which motivates zero-shot intent classification.

Classification General Classification +3

Clustering Uncertain Data via Representative Possible Worlds with Consistency Learning

no code implementations27 Sep 2019 Han Liu, Xianchao Zhang, Xiaotong Zhang, Qimai Li, Xiao-Ming Wu

However, there are two issues in existing possible world based algorithms: (1) They rely on all the possible worlds and treat them equally, but some marginal possible worlds may cause negative effects.

Clustering

Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs

1 code implementation26 Sep 2019 Qimai Li, Xiaotong Zhang, Han Liu, Quanyu Dai, Xiao-Ming Wu

Graph convolutional neural networks (GCN) have been the model of choice for graph representation learning, which is mainly due to the effective design of graph convolution that computes the representation of a node by aggregating those of its neighbors.

Attribute Clustering +3

AdvCodec: Towards A Unified Framework for Adversarial Text Generation

no code implementations25 Sep 2019 Boxin Wang, Hengzhi Pei, Han Liu, Bo Li

In particular, we propose a tree based autoencoder to encode discrete text data into continuous vector space, upon which we optimize the adversarial perturbation.

Adversarial Text Question Answering +3

Attributed Graph Learning with 2-D Graph Convolution

no code implementations25 Sep 2019 Qimai Li, Xiaotong Zhang, Han Liu, Xiao-Ming Wu

Graph convolutional neural networks have demonstrated promising performance in attributed graph learning, thanks to the use of graph convolution that effectively combines graph structures and node features for learning node representations.

Attribute Graph Learning +2

Fast Low-rank Metric Learning for Large-scale and High-dimensional Data

1 code implementation NeurIPS 2019 Han Liu, Zhizhong Han, Yu-Shen Liu, Ming Gu

Low-rank metric learning aims to learn better discrimination of data subject to low-rank constraints.

Metric Learning

Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding

no code implementations20 Jun 2019 Yutai Hou, Zhihan Zhou, Yijia Liu, Ning Wang, Wanxiang Che, Han Liu, Ting Liu

It calculates emission score with similarity based methods and obtains transition score with a specially designed transfer mechanism.

Few-Shot Learning named-entity-recognition +3

Attributed Graph Clustering via Adaptive Graph Convolution

1 code implementation4 Jun 2019 Xiaotong Zhang, Han Liu, Qimai Li, Xiao-Ming Wu

Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes.

Clustering Community Detection +2

GLAD: Learning Sparse Graph Recovery

1 code implementation ICLR 2020 Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinvas Aluru, Han Liu, Le Song

Recently, there is a surge of interest to learn algorithms directly based on data, and in this case, learn to map empirical covariance to the sparse precision matrix.

Inductive Bias

Estimating and Inferring the Maximum Degree of Stimulus-Locked Time-Varying Brain Connectivity Networks

no code implementations28 May 2019 Kean Ming Tan, Junwei Lu, Tong Zhang, Han Liu

To address this issue, neuroscientists have been measuring brain activity under natural viewing experiments in which the subjects are given continuous stimuli, such as watching a movie or listening to a story.

Experimental Design

Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees

1 code implementation ICLR 2020 Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song

We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces.

Vocal Bursts Intensity Prediction

Label Efficient Semi-Supervised Learning via Graph Filtering

1 code implementation CVPR 2019 Qimai Li, Xiao-Ming Wu, Han Liu, Xiaotong Zhang, Zhichao Guan

However, existing graph-based methods either are limited in their ability to jointly model graph structures and data features, such as the classical label propagation methods, or require a considerable amount of labeled data for training and validation due to high model complexity, such as the recent neural-network-based methods.

General Classification Graph Similarity

Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents

no code implementations6 Dec 2018 Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Başar

This work appears to be the first finite-sample analysis for batch MARL, a step towards rigorous theoretical understanding of general MARL algorithms in the finite-sample regime.

Multi-agent Reinforcement Learning reinforcement-learning +2

Sketching Method for Large Scale Combinatorial Inference

no code implementations NeurIPS 2018 Wei Sun, Junwei Lu, Han Liu

In order to test the hypotheses on their topological structures, we propose two adjacency matrix sketching frameworks: neighborhood sketching and subgraph sketching.

regression

Performance assessment of the deep learning technologies in grading glaucoma severity

no code implementations31 Oct 2018 Yi Zhen, Lei Wang, Han Liu, Jian Zhang, Jiantao Pu

Among these CNNs, the DenseNet had the highest classification accuracy (i. e., 75. 50%) based on pre-trained weights when using global ROIs, as compared to 65. 50% when using local ROIs.

SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images

no code implementations30 Oct 2018 Han Liu, Lei Wang, Yandong Nan, Faguang Jin, Qi. Wang, Jiantao Pu

Two CNN-based classification models were then used as feature extractors to obtain the discriminative features of the entire CXR images and the cropped lung region images.

General Classification Thoracic Disease Classification

Super-pixel cloud detection using Hierarchical Fusion CNN

no code implementations19 Oct 2018 Han Liu, Dan Zeng, Qi Tian

Secondly, super-pixel level database is used to train our cloud detection models based on CNN and deep forest.

Binary Classification Cloud Detection +2

Fully Implicit Online Learning

no code implementations25 Sep 2018 Chaobing Song, Ji Liu, Han Liu, Yong Jiang, Tong Zhang

Regularized online learning is widely used in machine learning applications.

High-Temperature Structure Detection in Ferromagnets

no code implementations21 Sep 2018 Yuan Cao, Matey Neykov, Han Liu

The goal is to distinguish whether the underlying graph is empty, i. e., the model consists of independent Rademacher variables, versus the alternative that the underlying graph contains a subgraph of a certain structure.

Vocal Bursts Intensity Prediction

TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game

2 code implementations19 Sep 2018 Peng Sun, Xinghai Sun, Lei Han, Jiechao Xiong, Qing Wang, Bo Li, Yang Zheng, Ji Liu, Yongsheng Liu, Han Liu, Tong Zhang

Both TStarBot1 and TStarBot2 are able to defeat the built-in AI agents from level 1 to level 10 in a full game (1v1 Zerg-vs-Zerg game on the AbyssalReef map), noting that level 8, level 9, and level 10 are cheating agents with unfair advantages such as full vision on the whole map and resource harvest boosting.

AI Agent Decision Making +3

A convex formulation for high-dimensional sparse sliced inverse regression

no code implementations17 Sep 2018 Kean Ming Tan, Zhaoran Wang, Tong Zhang, Han Liu, R. Dennis Cook

Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces covariates with a minimal set of their linear combinations without loss of information on the conditional distribution of the response given the covariates.

Dimensionality Reduction regression +2

Factorized Q-Learning for Large-Scale Multi-Agent Systems

no code implementations11 Sep 2018 Yong Chen, Ming Zhou, Ying Wen, Yaodong Yang, Yufeng Su, Wei-Nan Zhang, Dell Zhang, Jun Wang, Han Liu

Deep Q-learning has achieved a significant success in single-agent decision making tasks.

Multiagent Systems

Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes

no code implementations NeurIPS 2016 Chris Junchi Li, Zhaoran Wang, Han Liu

Despite the empirical success of nonconvex statistical optimization methods, their global dynamics, especially convergence to the desirable local minima, remain less well understood in theory.

Tensor Decomposition

Diffusion Approximations for Online Principal Component Estimation and Global Convergence

no code implementations NeurIPS 2017 Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang

In this paper, we propose to adopt the diffusion approximation tools to study the dynamics of Oja's iteration which is an online stochastic gradient descent method for the principal component analysis.

Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval

no code implementations21 Aug 2018 Jianqing Fan, Han Liu, Zhaoran Wang, Zhuoran Yang

We study the fundamental tradeoffs between statistical accuracy and computational tractability in the analysis of high dimensional heterogeneous data.

Clustering Retrieval

Graphical Nonconvex Optimization via an Adaptive Convex Relaxation

no code implementations ICML 2018 Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang

Our proposal is computationally tractable and produces an estimator that achieves the oracle rate of convergence.

The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference

no code implementations ICML 2018 Hao Lu, Yuan Cao, Zhuoran Yang, Junwei Lu, Han Liu, Zhaoran Wang

We study the hypothesis testing problem of inferring the existence of combinatorial structures in undirected graphical models.

Two-sample testing

Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications

1 code implementation ICLR 2019 Carson Eisenach, Haichuan Yang, Ji Liu, Han Liu

In the former, an agent learns a policy over $\mathbb{R}^d$ and in the latter, over a discrete set of actions each of which is parametrized by a continuous parameter.

continuous-control Continuous Control +2

Efficient, Certifiably Optimal Clustering with Applications to Latent Variable Graphical Models

2 code implementations1 Jun 2018 Carson Eisenach, Han Liu

Compared to the naive interior point method, our method reduces the computational complexity of solving the SDP from $\tilde{O}(d^7\log\epsilon^{-1})$ to $\tilde{O}(d^{6}K^{-2}\epsilon^{-1})$ arithmetic operations for an $\epsilon$-optimal solution.

Clustering

Feedback-Based Tree Search for Reinforcement Learning

no code implementations ICML 2018 Daniel R. Jiang, Emmanuel Ekwedike, Han Liu

Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of artificial intelligence (AI) application domains, we propose a model-based reinforcement learning (RL) technique that iteratively applies MCTS on batches of small, finite-horizon versions of the original infinite-horizon Markov decision process.

AI Agent Model-based Reinforcement Learning +3

Discrete Factorization Machines for Fast Feature-based Recommendation

1 code implementation6 May 2018 Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang

In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.

Binarization Quantization

Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents

5 code implementations ICML 2018 Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Başar

To this end, we propose two decentralized actor-critic algorithms with function approximation, which are applicable to large-scale MARL problems where both the number of states and the number of agents are massively large.

Multi-agent Reinforcement Learning reinforcement-learning +2

The Enemy Among Us: Detecting Hate Speech with Threats Based 'Othering' Language Embeddings

no code implementations23 Jan 2018 Wafa Alorainy, Pete Burnap, Han Liu, Matthew Williams

Offensive or antagonistic language targeted at individuals and social groups based on their personal characteristics (also known as cyber hate speech or cyberhate) has been frequently posted and widely circulated viathe World Wide Web.

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