Search Results for author: Xin Liu

Found 295 papers, 120 papers with code

How Powerful is Graph Filtering for Recommendation

no code implementations13 Jun 2024 Shaowen Peng, Xin Liu, Kazunari Sugiyama, Tsunenori Mine

Based on this observation, we propose a generalized graph normalization G^2N to adjust the sharpness of spectral distribution in order to redistribute data noise to assure that it can be removed by graph filtering without training.

FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion

no code implementations11 Jun 2024 Li-Wen Chang, Wenlei Bao, Qi Hou, Chengquan Jiang, Ningxin Zheng, Yinmin Zhong, Xuanrun Zhang, Zuquan Song, Ziheng Jiang, Haibin Lin, Xin Jin, Xin Liu

Overall, it can achieve up to 1. 24x speedups for training over Megatron-LM on a cluster of 128 GPUs with various GPU generations and interconnects, and up to 1. 66x and 1. 30x speedups for prefill and decoding inference over vLLM on a cluster with 8 GPUs with various GPU generations and interconnects.

Transforming Wearable Data into Health Insights using Large Language Model Agents

no code implementations10 Jun 2024 Mike A. Merrill, Akshay Paruchuri, Naghmeh Rezaei, Geza Kovacs, Javier Perez, Yun Liu, Erik Schenck, Nova Hammerquist, Jake Sunshine, Shyam Tailor, Kumar Ayush, Hao-Wei Su, Qian He, Cory Y. McLean, Mark Malhotra, Shwetak Patel, Jiening Zhan, Tim Althoff, Daniel McDuff, Xin Liu

Despite the proliferation of wearable health trackers and the importance of sleep and exercise to health, deriving actionable personalized insights from wearable data remains a challenge because doing so requires non-trivial open-ended analysis of these data.

Code Generation Information Retrieval +3

Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning

no code implementations20 May 2024 Xin Liu, Yaran Chen, Dongbin Zhao

Employing auxiliary tasks allows the agent to enhance visual representation in a targeted manner, thereby improving the sample efficiency and performance of downstream RL.

Continuous Control Reinforcement Learning (RL) +1

A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine

no code implementations14 May 2024 Hanguang Xiao, Feizhong Zhou, Xingyue Liu, Tianqi Liu, Zhipeng Li, Xin Liu, Xiaoxuan Huang

Since the release of ChatGPT and GPT-4, large language models (LLMs) and multimodal large language models (MLLMs) have garnered significant attention due to their powerful and general capabilities in understanding, reasoning, and generation, thereby offering new paradigms for the integration of artificial intelligence with medicine.

A Decoupling and Aggregating Framework for Joint Extraction of Entities and Relations

no code implementations14 May 2024 Yao Wang, Xin Liu, Weikun Kong, Hai-Tao Yu, Teeradaj Racharak, Kyoung-Sook Kim, Minh Le Nguyen

Second, information interaction mainly focuses on the two subtasks, leaving the fine-grained informtion interaction among the subtask-specific features of encoding subjects, relations, and objects unexplored.

named-entity-recognition Named Entity Recognition +1

Towards Subgraph Isomorphism Counting with Graph Kernels

no code implementations13 May 2024 Xin Liu, Weiqi Wang, Jiaxin Bai, Yangqiu Song

Subgraph isomorphism counting is known as #P-complete and requires exponential time to find the accurate solution.

Graph Classification Representation Learning

Disttack: Graph Adversarial Attacks Toward Distributed GNN Training

1 code implementation10 May 2024 Yuxiang Zhang, Xin Liu, Meng Wu, Wei Yan, Mingyu Yan, Xiaochun Ye, Dongrui Fan

In this study, we introduce Disttack, the first framework of adversarial attacks for distributed GNN training that leverages the characteristics of frequent gradient updates in a distributed system.

Adversarial Attack Graph Learning

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

1 code implementation7 May 2024 DeepSeek-AI, Aixin Liu, Bei Feng, Bin Wang, Bingxuan Wang, Bo Liu, Chenggang Zhao, Chengqi Dengr, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Hanwei Xu, Hao Yang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J. L. Cai, Jian Liang, JianZhong Guo, Jiaqi Ni, Jiashi Li, Jin Chen, Jingyang Yuan, Junjie Qiu, Junxiao Song, Kai Dong, Kaige Gao, Kang Guan, Lean Wang, Lecong Zhang, Lei Xu, Leyi Xia, Liang Zhao, Liyue Zhang, Meng Li, Miaojun Wang, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Mingming Li, Ning Tian, Panpan Huang, Peiyi Wang, Peng Zhang, Qihao Zhu, Qinyu Chen, Qiushi Du, R. J. Chen, R. L. Jin, Ruiqi Ge, Ruizhe Pan, Runxin Xu, Ruyi Chen, S. S. Li, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shaoqing Wu, Shengfeng Ye, Shirong Ma, Shiyu Wang, Shuang Zhou, Shuiping Yu, Shunfeng Zhou, Size Zheng, T. Wang, Tian Pei, Tian Yuan, Tianyu Sun, W. L. Xiao, Wangding Zeng, Wei An, Wen Liu, Wenfeng Liang, Wenjun Gao, Wentao Zhang, X. Q. Li, Xiangyue Jin, Xianzu Wang, Xiao Bi, Xiaodong Liu, Xiaohan Wang, Xiaojin Shen, Xiaokang Chen, Xiaosha Chen, Xiaotao Nie, Xiaowen Sun, Xiaoxiang Wang, Xin Liu, Xin Xie, Xingkai Yu, Xinnan Song, Xinyi Zhou, Xinyu Yang, Xuan Lu, Xuecheng Su, Y. Wu, Y. K. Li, Y. X. Wei, Y. X. Zhu, Yanhong Xu, Yanping Huang, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Li, Yaohui Wang, Yi Zheng, Yichao Zhang, Yiliang Xiong, Yilong Zhao, Ying He, Ying Tang, Yishi Piao, Yixin Dong, Yixuan Tan, Yiyuan Liu, Yongji Wang, Yongqiang Guo, Yuchen Zhu, Yuduan Wang, Yuheng Zou, Yukun Zha, Yunxian Ma, Yuting Yan, Yuxiang You, Yuxuan Liu, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhen Huang, Zhen Zhang, Zhenda Xie, Zhewen Hao, Zhihong Shao, Zhiniu Wen, Zhipeng Xu, Zhongyu Zhang, Zhuoshu Li, Zihan Wang, Zihui Gu, Zilin Li, Ziwei Xie

MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.

Language Modelling Reinforcement Learning (RL)

A quantile-based nonadditive fixed effects model

no code implementations6 May 2024 Xin Liu

I propose a quantile-based nonadditive fixed effects panel model to study heterogeneous causal effects.

Enhancing Micro Gesture Recognition for Emotion Understanding via Context-aware Visual-Text Contrastive Learning

1 code implementation3 May 2024 Deng Li, Bohao Xing, Xin Liu

In addition, instead of using handcrafted prompts for visual-text contrastive learning, we propose a novel module called Adaptive prompting to generate context-aware prompts.

Contrastive Learning Gesture Recognition +1

EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model

no code implementations1 May 2024 Deng Li, Xin Liu, Bohao Xing, Baiqiang Xia, Yuan Zong, Bihan Wen, Heikki Kälviäinen

In contrast, long sequential videos can reveal authentic emotions; 2) Previous studies commonly utilize various signals such as facial, speech, and even sensitive biological signals (e. g., electrocardiogram).

De-identification Emotion Recognition +2

Enhanced Language Model Truthfulness with Learnable Intervention and Uncertainty Expression

1 code implementation1 May 2024 Farima Fatahi Bayat, Xin Liu, H. V. Jagadish, Lu Wang

The adaptive nature of LITO counters the limitations of one-size-fits-all intervention methods, maximizing truthfulness by reflecting the model's internal knowledge only when it is confident.

Language Modelling Question Answering

NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding

no code implementations21 Apr 2024 Chunkit Chan, Cheng Jiayang, Yauwai Yim, Zheye Deng, Wei Fan, Haoran Li, Xin Liu, Hongming Zhang, Weiqi Wang, Yangqiu Song

Large Language Models (LLMs) have sparked substantial interest and debate concerning their potential emergence of Theory of Mind (ToM) ability.

RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation

no code implementations18 Apr 2024 Chao Jin, Zili Zhang, Xuanlin Jiang, Fangyue Liu, Xin Liu, Xuanzhe Liu, Xin Jin

We implement RAGCache and evaluate it on vLLM, a state-of-the-art LLM inference system and Faiss, a state-of-the-art vector database.

Retrieval

The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report

3 code implementations16 Apr 2024 Bin Ren, Nancy Mehta, Radu Timofte, Hongyuan Yu, Cheng Wan, Yuxin Hong, Bingnan Han, Zhuoyuan Wu, Yajun Zou, Yuqing Liu, Jizhe Li, Keji He, Chao Fan, Heng Zhang, Xiaolin Zhang, Xuanwu Yin, Kunlong Zuo, Bohao Liao, Peizhe Xia, Long Peng, Zhibo Du, Xin Di, Wangkai Li, Yang Wang, Wei Zhai, Renjing Pei, Jiaming Guo, Songcen Xu, Yang Cao, ZhengJun Zha, Yan Wang, Yi Liu, Qing Wang, Gang Zhang, Liou Zhang, Shijie Zhao, Long Sun, Jinshan Pan, Jiangxin Dong, Jinhui Tang, Xin Liu, Min Yan, Menghan Zhou, Yiqiang Yan, Yixuan Liu, Wensong Chan, Dehua Tang, Dong Zhou, Li Wang, Lu Tian, Barsoum Emad, Bohan Jia, Junbo Qiao, Yunshuai Zhou, Yun Zhang, Wei Li, Shaohui Lin, Shenglong Zhou, Binbin Chen, Jincheng Liao, Suiyi Zhao, Zhao Zhang, Bo wang, Yan Luo, Yanyan Wei, Feng Li, Mingshen Wang, Yawei Li, Jinhan Guan, Dehua Hu, Jiawei Yu, Qisheng Xu, Tao Sun, Long Lan, Kele Xu, Xin Lin, Jingtong Yue, Lehan Yang, Shiyi Du, Lu Qi, Chao Ren, Zeyu Han, YuHan Wang, Chaolin Chen, Haobo Li, Mingjun Zheng, Zhongbao Yang, Lianhong Song, Xingzhuo Yan, Minghan Fu, Jingyi Zhang, Baiang Li, Qi Zhu, Xiaogang Xu, Dan Guo, Chunle Guo, Jiadi Chen, Huanhuan Long, Chunjiang Duanmu, Xiaoyan Lei, Jie Liu, Weilin Jia, Weifeng Cao, Wenlong Zhang, Yanyu Mao, Ruilong Guo, Nihao Zhang, Qian Wang, Manoj Pandey, Maksym Chernozhukov, Giang Le, Shuli Cheng, Hongyuan Wang, Ziyan Wei, Qingting Tang, Liejun Wang, Yongming Li, Yanhui Guo, Hao Xu, Akram Khatami-Rizi, Ahmad Mahmoudi-Aznaveh, Chih-Chung Hsu, Chia-Ming Lee, Yi-Shiuan Chou, Amogh Joshi, Nikhil Akalwadi, Sampada Malagi, Palani Yashaswini, Chaitra Desai, Ramesh Ashok Tabib, Ujwala Patil, Uma Mudenagudi

In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.

Image Super-Resolution

DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature Noise

1 code implementation14 Apr 2024 Tai Hasegawa, Sukwon Yun, Xin Liu, Yin Jun Phua, Tsuyoshi Murata

Leveraging these modified representations, DEGNN subsequently addresses downstream tasks, ensuring robustness against noise present in both edges and node features of real-world graphs.

Graph Neural Network Graph structure learning +1

Future-Proofing Class Incremental Learning

no code implementations4 Apr 2024 Quentin Jodelet, Xin Liu, Yin Jun Phua, Tsuyoshi Murata

Exemplar-Free Class Incremental Learning is a highly challenging setting where replay memory is unavailable.

Class Incremental Learning Incremental Learning

EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs

1 code implementation30 Mar 2024 Cheng Jiayang, Lin Qiu, Chunkit Chan, Xin Liu, Yangqiu Song, Zheng Zhang

In this work, we propose an initial comprehensive framework called EventGround, which aims to tackle the problem of grounding free-texts to eventuality-centric KGs for contextualized narrative reasoning.

Graph Neural Network Knowledge Graphs +2

Decentralized Stochastic Subgradient Methods for Nonsmooth Nonconvex Optimization

no code implementations18 Mar 2024 Siyuan Zhang, Nachuan Xiao, Xin Liu

Furthermore, we establish that our proposed framework encompasses a wide range of existing efficient decentralized subgradient methods, including decentralized stochastic subgradient descent (DSGD), DSGD with gradient-tracking technique (DSGD-T), and DSGD with momentum (DSGDm).

Benchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Study

1 code implementation15 Mar 2024 Chenguang Wang, Ruoxi Jia, Xin Liu, Dawn Song

We show that CLIP leads to a significant robustness drop compared to supervised ImageNet models on our benchmark, especially under synthetic distribution shift and adversarial attacks.

Benchmarking

Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection

no code implementations15 Mar 2024 Rui Zhang, Dawei Cheng, Xin Liu, Jie Yang, Yi Ouyang, Xian Wu, Yefeng Zheng

We find that in graph anomaly detection, the homophily distribution differences between different classes are significantly greater than those in homophilic and heterophilic graphs.

Graph Anomaly Detection Graph Classification +2

Answering Diverse Questions via Text Attached with Key Audio-Visual Clues

1 code implementation11 Mar 2024 Qilang Ye, Zitong Yu, Xin Liu

Audio-visual question answering (AVQA) requires reference to video content and auditory information, followed by correlating the question to predict the most precise answer.

Audio-visual Question Answering Audio-Visual Question Answering (AVQA) +3

Advancing Generalizable Remote Physiological Measurement through the Integration of Explicit and Implicit Prior Knowledge

1 code implementation11 Mar 2024 Yuting Zhang, Hao Lu, Xin Liu, Yingcong Chen, Kaishun Wu

Remote photoplethysmography (rPPG) is a promising technology that captures physiological signals from face videos, with potential applications in medical health, emotional computing, and biosecurity recognition.

Domain Generalization

ACT-MNMT Auto-Constriction Turning for Multilingual Neural Machine Translation

no code implementations11 Mar 2024 Shaojie Dai, Xin Liu, Ping Luo, Yue Yu

Large language model (LLM) has achieved promising performance in multilingual machine translation tasks through zero/few-shot prompts or prompt-tuning.

Language Modelling Large Language Model +2

Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack

no code implementations10 Mar 2024 Xin Liu, Yuxiang Zhang, Meng Wu, Mingyu Yan, Kun He, Wei Yan, Shirui Pan, Xiaochun Ye, Dongrui Fan

It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i. e., graph data augmentation and attack.

Data Augmentation Graph Neural Network

AUFormer: Vision Transformers are Parameter-Efficient Facial Action Unit Detectors

1 code implementation7 Mar 2024 Kaishen Yuan, Zitong Yu, Xin Liu, Weicheng Xie, Huanjing Yue, Jingyu Yang

Facial Action Units (AU) is a vital concept in the realm of affective computing, and AU detection has always been a hot research topic.

Facial Action Unit Detection Transfer Learning

Multi-modal Attribute Prompting for Vision-Language Models

no code implementations1 Mar 2024 Xin Liu, Jiamin Wu, Tianzhu Zhang

To address this issue, we propose a Multi-modal Attribute Prompting method (MAP) by jointly exploring textual attribute prompting, visual attribute prompting, and attribute-level alignment.

Attribute

A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection

no code implementations29 Feb 2024 Chao Hao, Zitong Yu, Xin Liu, Jun Xu, Huanjing Yue, Jingyu Yang

Camouflaged object detection (COD) and salient object detection (SOD) are two distinct yet closely-related computer vision tasks widely studied during the past decades.

Object object-detection +2

FedUV: Uniformity and Variance for Heterogeneous Federated Learning

no code implementations CVPR 2024 Ha Min Son, Moon-Hyun Kim, Tai-Myoung Chung, Chao Huang, Xin Liu

Based on this finding, we introduce two regularization terms for local training to continuously emulate IID settings: (1) variance in the dimension-wise probability distribution of the classifier and (2) hyperspherical uniformity of representations of the encoder.

Federated Learning

Convergence Analysis of Split Federated Learning on Heterogeneous Data

no code implementations23 Feb 2024 Pengchao Han, Chao Huang, Geng Tian, Ming Tang, Xin Liu

We further extend the analysis to non-convex objectives and where some clients may be unavailable during training.

Federated Learning

Safety of Multimodal Large Language Models on Images and Text

1 code implementation1 Feb 2024 Xin Liu, Yichen Zhu, Yunshi Lan, Chao Yang, Yu Qiao

In this paper, we systematically survey current efforts on the evaluation, attack, and defense of MLLMs' safety on images and text.

Fast Adversarial Training against Textual Adversarial Attacks

no code implementations23 Jan 2024 Yichen Yang, Xin Liu, Kun He

Based on the observation that the adversarial perturbations crafted by single-step and multi-step gradient ascent are similar, FAT uses single-step gradient ascent to craft adversarial examples in the embedding space to expedite the training process.

Adversarial Defense Adversarial Robustness

CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning

2 code implementations14 Jan 2024 Weiqi Wang, Tianqing Fang, Chunyang Li, Haochen Shi, Wenxuan Ding, Baixuan Xu, Zhaowei Wang, Jiaxin Bai, Xin Liu, Jiayang Cheng, Chunkit Chan, Yangqiu Song

The sequential process of conceptualization and instantiation is essential to generalizable commonsense reasoning as it allows the application of existing knowledge to unfamiliar scenarios.

Adversarially Trained Actor Critic for offline CMDPs

no code implementations1 Jan 2024 Honghao Wei, Xiyue Peng, Xin Liu, Arnob Ghosh

Theoretically, we demonstrate that when the actor employs a no-regret optimization oracle, SATAC achieves two guarantees: (i) For the first time in the offline RL setting, we establish that SATAC can produce a policy that outperforms the behavior policy while maintaining the same level of safety, which is critical to designing an algorithm for offline RL.

Continuous Control Offline RL +1

Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation

no code implementations25 Dec 2023 Jiaxin Bai, Yicheng Wang, Tianshi Zheng, Yue Guo, Xin Liu, Yangqiu Song

Although many applications require the use of knowledge for explanations, the utilization of abductive reasoning in conjunction with structured knowledge, such as a knowledge graph, remains largely unexplored.

Knowledge Graphs Logical Reasoning

Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns

1 code implementation NeurIPS 2023 Xin Liu, Zheng Li, Yifan Gao, Jingfeng Yang, Tianyu Cao, Zhengyang Wang, Bing Yin, Yangqiu Song

The goal of session-based recommendation in E-commerce is to predict the next item that an anonymous user will purchase based on the browsing and purchase history.

Attribute Session-Based Recommendations

Safe Reinforcement Learning with Instantaneous Constraints: The Role of Aggressive Exploration

no code implementations22 Dec 2023 Honghao Wei, Xin Liu, Lei Ying

This paper studies safe Reinforcement Learning (safe RL) with linear function approximation and under hard instantaneous constraints where unsafe actions must be avoided at each step.

4k reinforcement-learning +1

AutoAugment Input Transformation for Highly Transferable Targeted Attacks

no code implementations21 Dec 2023 Haobo Lu, Xin Liu, Kun He

However, few of them are dedicated to input transformation. In this work, we observe a positive correlation between the logit/probability of the target class and diverse input transformation methods in targeted attacks.

Adversarial Attack

MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models

1 code implementation29 Nov 2023 Xin Liu, Yichen Zhu, Jindong Gu, Yunshi Lan, Chao Yang, Yu Qiao

The security concerns surrounding Large Language Models (LLMs) have been extensively explored, yet the safety of Multimodal Large Language Models (MLLMs) remains understudied.

From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models

no code implementations21 Nov 2023 Zachary Englhardt, Chengqian Ma, Margaret E. Morris, Xuhai "Orson" Xu, Chun-Cheng Chang, Lianhui Qin, Daniel McDuff, Xin Liu, Shwetak Patel, Vikram Iyer

Passively collected behavioral health data from ubiquitous sensors holds significant promise to provide mental health professionals insights from patient's daily lives; however, developing analysis tools to use this data in clinical practice requires addressing challenges of generalization across devices and weak or ambiguous correlations between the measured signals and an individual's mental health.

Decision Making

ALPHA: AnomaLous Physiological Health Assessment Using Large Language Models

1 code implementation21 Nov 2023 Jiankai Tang, Kegang Wang, Hongming Hu, Xiyuxing Zhang, Peiyu Wang, Xin Liu, Yuntao Wang

Our findings reveal that LLMs exhibit exceptional performance in determining medical indicators, including a Mean Absolute Error (MAE) of less than 1 beat per minute for heart rate and less than 1% for oxygen saturation (SpO2).

Heart rate estimation Specificity

4K-Resolution Photo Exposure Correction at 125 FPS with ~8K Parameters

1 code implementation15 Nov 2023 Yijie Zhou, Chao Li, Jin Liang, Tianyi Xu, Xin Liu, Jun Xu

The illumination of improperly exposed photographs has been widely corrected using deep convolutional neural networks or Transformers.

4k 8k

AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph

1 code implementation15 Nov 2023 Zhaowei Wang, Haochen Shi, Weiqi Wang, Tianqing Fang, Hongming Zhang, Sehyun Choi, Xin Liu, Yangqiu Song

Cognitive research indicates that abstraction ability is essential in human intelligence, which remains under-explored in language models.

Benchmarking

Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data

no code implementations9 Nov 2023 Yuxuan Ou, Yuzhe Zhang, Yuntang Wang, Shwetak Patel, Daniel McDuf, Yuzhe Yang, Xin Liu

However, there exists a significant gap between synthetic and real-world data, which hinders the generalization of neural models trained on these synthetic datasets.

PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models

1 code implementation7 Nov 2023 Haoran Li, Dadi Guo, Donghao Li, Wei Fan, Qi Hu, Xin Liu, Chunkit Chan, Duanyi Yao, Yuan YAO, Yangqiu Song

Lastly, PrivLM-Bench performs existing privacy attacks on LMs with pre-defined privacy objectives as the empirical evaluation results.

Privacy Preserving

IBADR: an Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU models

no code implementations1 Nov 2023 Xiaoyue Wang, Xin Liu, Lijie Wang, Yaoxiang Wang, Jinsong Su, Hua Wu

Then, we pair each sample with a bias indicator representing its bias degree, and use these extended samples to train a sample generator.

Natural Language Understanding

Recaptured Raw Screen Image and Video Demoiréing via Channel and Spatial Modulations

1 code implementation NeurIPS 2023 Huanjing Yue, Yijia Cheng, Xin Liu, Jingyu Yang

The spatial modulation utilizes the feature with large receptive field to modulate the feature with small receptive field.

LitCab: Lightweight Language Model Calibration over Short- and Long-form Responses

1 code implementation30 Oct 2023 Xin Liu, Muhammad Khalifa, Lu Wang

For evaluation, we construct CaT, a benchmark consisting of eight text generation tasks, covering responses ranging from short phrases to paragraphs.

Language Modelling Text Generation

Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection

1 code implementation18 Oct 2023 Zheye Deng, Weiqi Wang, Zhaowei Wang, Xin Liu, Yangqiu Song

Commonsense Knowledge Graphs (CSKGs) are crucial for commonsense reasoning, yet constructing them through human annotations can be costly.

Denoising Knowledge Graphs +1

QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering

1 code implementation17 Oct 2023 Haochen Shi, Weiqi Wang, Tianqing Fang, Baixuan Xu, Wenxuan Ding, Xin Liu, Yangqiu Song

Zero-shot commonsense Question-Answering (QA) requires models to reason about general situations beyond specific benchmarks.

Question Answering

On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networks

no code implementations9 Oct 2023 Xin Liu, Wei Li, Dazhi Zhan, Yu Pan, Xin Ma, Yu Ding, Zhisong Pan

Federated learning (FL) is a widely employed distributed paradigm for collaboratively training machine learning models from multiple clients without sharing local data.

Federated Learning

Balancing Both Behavioral Quality and Diversity in Unsupervised Skill Discovery

no code implementations29 Sep 2023 Xin Liu, Yaran Chen, Dongbin Zhao

It contains a novel diversity reward based on contrastive learning to effectively drive agents to discern existing skills, and a particle-based exploration reward to access and learn new behaviors.

Contrastive Learning

Self-Consistent Narrative Prompts on Abductive Natural Language Inference

1 code implementation15 Sep 2023 Chunkit Chan, Xin Liu, Tsz Ho Chan, Jiayang Cheng, Yangqiu Song, Ginny Wong, Simon See

However, the inter-sentential coherence and the model consistency have not been well exploited in the previous works on this task.

Language Modelling Natural Language Inference

Federated Linear Bandit Learning via Over-the-Air Computation

no code implementations25 Aug 2023 Jiali Wang, Yuning Jiang, Xin Liu, Ting Wang, Yuanming Shi

In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise.

Video BagNet: short temporal receptive fields increase robustness in long-term action recognition

1 code implementation22 Aug 2023 Ombretta Strafforello, Xin Liu, Klamer Schutte, Jan van Gemert

Previous work on long-term video action recognition relies on deep 3D-convolutional models that have a large temporal receptive field (RF).

Action Recognition Temporal Action Localization

Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks

no code implementations17 Aug 2023 Junkai Qian, Yuning Jiang, Xin Liu, Qing Wang, Ting Wang, Yuanming Shi, Wei Chen

To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed.

reinforcement-learning

Edit Temporal-Consistent Videos with Image Diffusion Model

no code implementations17 Aug 2023 Yuanzhi Wang, Yong Li, Xiaoya Zhang, Xin Liu, Anbo Dai, Antoni B. Chan, Zhen Cui

In addition to the utilization of a pretrained T2I 2D Unet for spatial content manipulation, we establish a dedicated temporal Unet architecture to faithfully capture the temporal coherence of the input video sequences.

Video Temporal Consistency

Dual-Stream Diffusion Net for Text-to-Video Generation

no code implementations16 Aug 2023 Binhui Liu, Xin Liu, Anbo Dai, Zhiyong Zeng, Dan Wang, Zhen Cui, Jian Yang

In particular, the designed two diffusion streams, video content and motion branches, could not only run separately in their private spaces for producing personalized video variations as well as content, but also be well-aligned between the content and motion domains through leveraging our designed cross-transformer interaction module, which would benefit the smoothness of generated videos.

Text-to-Video Generation Video Generation

CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks

1 code implementation9 Aug 2023 Hongjun Zhu, Shun Yuan, Xin Liu, Kuo Chen, Chaolong Jia, Ying Qian

Existing approaches for information cascade prediction fall into three main categories: feature-driven methods, point process-based methods, and deep learning-based methods.

Multi-Task Learning

Exploring and Characterizing Large Language Models For Embedded System Development and Debugging

no code implementations7 Jul 2023 Zachary Englhardt, Richard Li, Dilini Nissanka, Zhihan Zhang, Girish Narayanswamy, Joseph Breda, Xin Liu, Shwetak Patel, Vikram Iyer

We leverage this finding to study how human programmers interact with these tools, and develop an human-AI based software engineering workflow for building embedded systems.

Class-Incremental Learning using Diffusion Model for Distillation and Replay

no code implementations30 Jun 2023 Quentin Jodelet, Xin Liu, Yin Jun Phua, Tsuyoshi Murata

Experiments on the competitive benchmarks CIFAR100, ImageNet-Subset, and ImageNet demonstrate how this new approach can be used to further improve the performance of state-of-the-art methods for class-incremental learning on large scale datasets.

Class Incremental Learning Incremental Learning

rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement

1 code implementation4 Jun 2023 Xin Liu, Yuting Zhang, Zitong Yu, Hao Lu, Huanjing Yue, Jingyu Yang

However, they focus on the contrastive learning between samples, which neglect the inherent self-similar prior in physiological signals and seem to have a limited ability to cope with noisy.

Contrastive Learning

A Simple yet Effective Self-Debiasing Framework for Transformer Models

1 code implementation2 Jun 2023 Xiaoyue Wang, Lijie Wang, Xin Liu, Suhang Wu, Jinsong Su, Hua Wu

In this way, the top-layer sentence representation will be trained to ignore the common biased features encoded by the low-layer sentence representation and focus on task-relevant unbiased features.

Natural Language Understanding Sentence

Optimizing Airbnb Search Journey with Multi-task Learning

no code implementations28 May 2023 Chun How Tan, Austin Chan, Malay Haldar, Jie Tang, Xin Liu, Mustafa Abdool, Huiji Gao, Liwei He, Sanjeev Katariya

The long and exploratory nature of the search journey, as well as the need to balance both guest and host preferences, present unique challenges for Airbnb search ranking.

Multi-Task Learning

CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering

1 code implementation24 May 2023 Weiqi Wang, Tianqing Fang, Wenxuan Ding, Baixuan Xu, Xin Liu, Yangqiu Song, Antoine Bosselut

The task of zero-shot commonsense question answering evaluates models on their capacity to reason about general scenarios beyond those presented in specific datasets.

Question Answering

BOLT: Fast Energy-based Controlled Text Generation with Tunable Biases

2 code implementations19 May 2023 Xin Liu, Muhammad Khalifa, Lu Wang

Energy-based models (EBMs) have gained popularity for controlled text generation due to their high applicability to a wide range of constraints.

Text Generation

DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition

1 code implementation6 May 2023 Chunkit Chan, Xin Liu, Jiayang Cheng, Zihan Li, Yangqiu Song, Ginny Y. Wong, Simon See

Implicit Discourse Relation Recognition (IDRR) is a sophisticated and challenging task to recognize the discourse relations between the arguments with the absence of discourse connectives.

Relation text-classification +1

Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees

no code implementations6 May 2023 Nachuan Xiao, Xiaoyin Hu, Xin Liu, Kim-Chuan Toh

In this paper, we present a comprehensive study on the convergence properties of Adam-family methods for nonsmooth optimization, especially in the training of nonsmooth neural networks.

Walk4Me: Telehealth Community Mobility Assessment, An Automated System for Early Diagnosis and Disease Progression

no code implementations5 May 2023 Albara Ah Ramli, Xin Liu, Erik K. Henricson

Our system achieves this by 1) enabling early diagnosis, 2) identifying early indicators of clinical severity, and 3) quantifying and tracking the progression of the disease across the ambulatory phase of the disease.

Decision Making

Recognizable Information Bottleneck

1 code implementation28 Apr 2023 Yilin Lyu, Xin Liu, Mingyang Song, Xinyue Wang, Yaxin Peng, Tieyong Zeng, Liping Jing

The recent PAC-Bayes IB uses information complexity instead of information compression to establish a connection with the mutual information generalization bound.

ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations

no code implementations28 Apr 2023 Chunkit Chan, Jiayang Cheng, Weiqi Wang, Yuxin Jiang, Tianqing Fang, Xin Liu, Yangqiu Song

This paper aims to quantitatively evaluate the performance of ChatGPT, an interactive large language model, on inter-sentential relations such as temporal relations, causal relations, and discourse relations.

Discourse Parsing In-Context Learning +6

SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI

no code implementations11 Apr 2023 Zhuo-Xu Cui, Chentao Cao, Yue Wang, Sen Jia, Jing Cheng, Xin Liu, Hairong Zheng, Dong Liang, Yanjie Zhu

To overcome this challenge, we introduce a novel approach called SPIRiT-Diffusion, which is a diffusion model for k-space interpolation inspired by the iterative self-consistent SPIRiT method.

Image Generation MRI Reconstruction

PIAT: Parameter Interpolation based Adversarial Training for Image Classification

no code implementations24 Mar 2023 Kun He, Xin Liu, Yichen Yang, Zhou Qin, Weigao Wen, Hui Xue, John E. Hopcroft

Besides, we suggest to use the Normalized Mean Square Error (NMSE) to further improve the robustness by aligning the clean and adversarial examples.

Classification Image Classification

Motion Matters: Neural Motion Transfer for Better Camera Physiological Measurement

1 code implementation21 Mar 2023 Akshay Paruchuri, Xin Liu, Yulu Pan, Shwetak Patel, Daniel McDuff, Soumyadip Sengupta

Our findings illustrate the usefulness of motion transfer as a data augmentation technique for improving the generalization of models for camera-based physiological sensing.

Data Augmentation Photoplethysmography (PPG)

BigSmall: Efficient Multi-Task Learning for Disparate Spatial and Temporal Physiological Measurements

2 code implementations21 Mar 2023 Girish Narayanswamy, Yujia Liu, Yuzhe Yang, Chengqian Ma, Xin Liu, Daniel McDuff, Shwetak Patel

As an example, perception occurs at different scales both spatially and temporally, suggesting that the extraction of salient visual information may be made more effective by paying attention to specific features at varying scales.

Multi-Task Learning

Facial Affect Recognition based on Transformer Encoder and Audiovisual Fusion for the ABAW5 Challenge

no code implementations16 Mar 2023 Ziyang Zhang, Liuwei An, Zishun Cui, Ao Xu, Tengteng Dong, Yueqi Jiang, Jingyi Shi, Xin Liu, Xiao Sun, Meng Wang

In this paper, we present our solutions for the 5th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW), which includes four sub-challenges of Valence-Arousal (VA) Estimation, Expression (Expr) Classification, Action Unit (AU) Detection and Emotional Reaction Intensity (ERI) Estimation.

Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning

1 code implementation13 Mar 2023 Cong Cao, Huanjing Yue, Xin Liu, Jingyu Yang

We construct a large-scale unpaired HDR-LDR video dataset to facilitate the unsupervised training process for video tone mapping.

Contrastive Learning Tone Mapping

X-ray Spectral Estimation using Dictionary Learning

no code implementations27 Feb 2023 Wenrui Li, Venkatesh Sridhar, K. Aditya Mohan, Saransh Singh, Jean-Baptiste Forien, Xin Liu, Gregery T. Buzzard, Charles A. Bouman

As computational tools for X-ray computed tomography (CT) become more quantitatively accurate, knowledge of the source-detector spectral response is critical for quantitative system-independent reconstruction and material characterization capabilities.

Computed Tomography (CT) Dictionary Learning

Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing

no code implementations11 Feb 2023 Zitong Yu, Rizhao Cai, Yawen Cui, Xin Liu, Yongjian Hu, Alex Kot

In this paper, we investigate three key factors (i. e., inputs, pre-training, and finetuning) in ViT for multimodal FAS with RGB, Infrared (IR), and Depth.

Face Anti-Spoofing

Cross-domain Random Pre-training with Prototypes for Reinforcement Learning

no code implementations11 Feb 2023 Xin Liu, Yaran Chen, Haoran Li, Boyu Li, Dongbin Zhao

CRPTpro significantly outperforms the next best Proto-RL(C) on 11/12 cross-domain downstream tasks with only 54\% wall-clock pre-training time, exhibiting state-of-the-art pre-training performance with greatly improved pre-training efficiency.

reinforcement-learning Reinforcement Learning (RL) +1

MMPD: Multi-Domain Mobile Video Physiology Dataset

2 code implementations8 Feb 2023 Jiankai Tang, Kequan Chen, Yuntao Wang, Yuanchun Shi, Shwetak Patel, Daniel McDuff, Xin Liu

Second, most datasets are relatively small and therefore are limited in diversity, both in appearance (e. g., skin tone), behaviors (e. g., motion) and environment (e. g., lighting conditions).

Descriptive

Online Nonstochastic Control with Adversarial and Static Constraints

no code implementations5 Feb 2023 Xin Liu, Zixian Yang, Lei Ying

This subroutine also achieves the state-of-the-art regret and constraint violation bounds for constrained online convex optimization problems, which is of independent interest.

Progressive Neighbor Consistency Mining for Correspondence Pruning

1 code implementation CVPR 2023 Xin Liu, Jufeng Yang

In the end, we develop a Neighbor Consistency Mining Network (NCMNet) for accurately recovering camera poses and identifying inliers.

C2ST: Cross-Modal Contextualized Sequence Transduction for Continuous Sign Language Recognition

no code implementations ICCV 2023 Huaiwen Zhang, Zihang Guo, Yang Yang, Xin Liu, De Hu

In this paper, we propose a Cross-modal Contextualized Sequence Transduction (C2ST) for CSLR, which effectively incorporates the knowledge of gloss sequence into the process of video representation learning and sequence transduction.

Language Modelling Representation Learning +1

Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems

no code implementations13 Dec 2022 Xin Liu, Honghao Wei, Lei Ying

The proposed algorithm is distributed in two aspects: (i) the learned policy is a distributed policy that maps a local state of an agent to its local action and (ii) the learning/training is distributed, during which each agent updates its policy based on its own and neighbors' information.

Multi-agent Reinforcement Learning reinforcement-learning +1

On the Discredibility of Membership Inference Attacks

no code implementations6 Dec 2022 Shahbaz Rezaei, Xin Liu

We argue that current membership inference attacks can identify memorized subpopulations, but they cannot reliably identify which exact sample in the subpopulation was used during the training.

Efficient stereo matching on embedded GPUs with zero-means cross correlation

no code implementations1 Dec 2022 Qiong Chang, Aolong Zha, Weimin WANG, Xin Liu, Masaki Onishi, Lei Lei, Meng Joo Er, Tsutomu Maruyama

By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1, 280x384 pixel images with a maximum disparity of 128.

Stereo Matching

Rectified Pessimistic-Optimistic Learning for Stochastic Continuum-armed Bandit with Constraints

no code implementations27 Nov 2022 Hengquan Guo, Qi Zhu, Xin Liu

This paper studies the problem of stochastic continuum-armed bandit with constraints (SCBwC), where we optimize a black-box reward function $f(x)$ subject to a black-box constraint function $g(x)\leq 0$ over a continuous space $\mathcal X$.

Gaussian Processes

Federated Learning Hyper-Parameter Tuning from a System Perspective

1 code implementation24 Nov 2022 Huanle Zhang, Lei Fu, Mi Zhang, Pengfei Hu, Xiuzhen Cheng, Prasant Mohapatra, Xin Liu

In this paper, we propose FedTune, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements in FL training.

Federated Learning

Quantifying the Impact of Label Noise on Federated Learning

no code implementations15 Nov 2022 Shuqi Ke, Chao Huang, Xin Liu

Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local (human-generated) datasets.

Federated Learning

Getting the Most out of Simile Recognition

no code implementations11 Nov 2022 Xiaoyue Wang, Linfeng Song, Xin Liu, Chulun Zhou, Jinsong Su

Simile recognition involves two subtasks: simile sentence classification that discriminates whether a sentence contains simile, and simile component extraction that locates the corresponding objects (i. e., tenors and vehicles).

POS Sentence +1

Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform

1 code implementation7 Nov 2022 Huiru Xiao, Xin Liu, Yangqiu Song, Ginny Y. Wong, Simon See

However, the performance of the hyperbolic KG embedding models for non-transitive relations is still unpromising, while the complex hyperbolic embeddings do not deal with multi-relations.

Knowledge Graph Embeddings

Client Selection in Federated Learning: Principles, Challenges, and Opportunities

no code implementations3 Nov 2022 Lei Fu, Huanle Zhang, Ge Gao, Mi Zhang, Xin Liu

As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) has received tremendous attention from both industry and academia.

Fairness Federated Learning +1

Opportunistic Episodic Reinforcement Learning

no code implementations24 Oct 2022 Xiaoxiao Wang, Nader Bouacida, Xueying Guo, Xin Liu

In this paper, we propose and study opportunistic reinforcement learning - a new variant of reinforcement learning problems where the regret of selecting a suboptimal action varies under an external environmental condition known as the variation factor.

reinforcement-learning Reinforcement Learning (RL)

Self-Supervised Learning via Maximum Entropy Coding

1 code implementation20 Oct 2022 Xin Liu, Zhongdao Wang, YaLi Li, Shengjin Wang

To cope with this issue, we propose Maximum Entropy Coding (MEC), a more principled objective that explicitly optimizes on the structure of the representation, so that the learned representation is less biased and thus generalizes better to unseen downstream tasks.

Instance Segmentation object-detection +4

GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs

1 code implementation19 Oct 2022 Xin Liu, Xiaofei Shao, Bo wang, YaLi Li, Shengjin Wang

First, unlike previous methods, we leverage convolution neural networks as well as graph neural networks in a complementary way for geometric representation learning.

Autonomous Driving Depth Completion +1

Not All Neighbors are Friendly: Learning to Choose Hop Features to Improve Node Classification

1 code implementation CIKM 2022 Sunil Kumar Maurya, Xin Liu, Tsuyoshi Murata

With extensive experiments, we show that our proposed model outperforms the state-of-the-art GNN models with remarkable improvements up to 27. 8%.

Node Classification

MMTSA: Multimodal Temporal Segment Attention Network for Efficient Human Activity Recognition

no code implementations14 Oct 2022 Ziqi Gao, Yuntao Wang, Jianguo Chen, Junliang Xing, Shwetak Patel, Xin Liu, Yuanchun Shi

The efficiency evaluation on an edge device showed that MMTSA achieved significantly better accuracy, lower computational load, and lower inference latency than SOTA methods.

Human Activity Recognition

SimPer: Simple Self-Supervised Learning of Periodic Targets

1 code implementation6 Oct 2022 Yuzhe Yang, Xin Liu, Jiang Wu, Silviu Borac, Dina Katabi, Ming-Zher Poh, Daniel McDuff

From human physiology to environmental evolution, important processes in nature often exhibit meaningful and strong periodic or quasi-periodic changes.

Inductive Bias Self-Supervised Learning

Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation

1 code implementation23 Sep 2022 Zhongwei Wan, Xin Liu, Benyou Wang, Jiezhong Qiu, Boyu Li, Ting Guo, Guangyong Chen, Yang Wang

The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism.

Collaborative Filtering Contrastive Learning +1

Rethinking Efficiency and Redundancy in Training Large-scale Graphs

no code implementations2 Sep 2022 Xin Liu, Xunbin Xiong, Mingyu Yan, Runzhen Xue, Shirui Pan, Xiaochun Ye, Dongrui Fan

Thereby, we propose to drop redundancy and improve efficiency of training large-scale graphs with GNNs, by rethinking the inherent characteristics in a graph.

Provable Acceleration of Nesterov's Accelerated Gradient Method over Heavy Ball Method in Training Over-Parameterized Neural Networks

no code implementations8 Aug 2022 Xin Liu, Wei Tao, Wei Li, Dazhi Zhan, Jun Wang, Zhisong Pan

Due to its simplicity and efficiency, the first-order gradient method has been extensively employed in training neural networks.

BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation

no code implementations18 Jul 2022 Yi Zeng, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yiting Dong, Enmeng Lu, Qian Zhang, Yinqian Sun, Qian Liang, Yuxuan Zhao, Zhuoya Zhao, Hongjian Fang, Yuwei Wang, Yang Li, Xin Liu, Chengcheng Du, Qingqun Kong, Zizhe Ruan, Weida Bi

These brain-inspired AI models have been effectively validated on various supervised, unsupervised, and reinforcement learning tasks, and they can be used to enable AI models to be with multiple brain-inspired cognitive functions.

Decision Making

Brain-inspired Graph Spiking Neural Networks for Commonsense Knowledge Representation and Reasoning

no code implementations11 Jul 2022 Hongjian Fang, Yi Zeng, Jianbo Tang, Yuwei Wang, Yao Liang, Xin Liu

For the fields of neuroscience and cognitive science, the work in this paper provided the foundation of computational modeling for further exploration of the way the human brain represents commonsense knowledge.

Enhancing Local Geometry Learning for 3D Point Cloud via Decoupling Convolution

no code implementations4 Jul 2022 Haoyi Xiu, Xin Liu, Weimin WANG, Kyoung-Sook Kim, Takayuki Shinohara, Qiong Chang, Masashi Matsuoka

Modeling the local surface geometry is challenging in 3D point cloud understanding due to the lack of connectivity information.

Cross-Silo Federated Learning: Challenges and Opportunities

no code implementations26 Jun 2022 Chao Huang, Jianwei Huang, Xin Liu

Federated learning (FL) is an emerging technology that enables the training of machine learning models from multiple clients while keeping the data distributed and private.

Federated Learning

FedSSO: A Federated Server-Side Second-Order Optimization Algorithm

no code implementations20 Jun 2022 Xin Ma, Renyi Bao, Jinpeng Jiang, Yang Liu, Arthur Jiang, Jun Yan, Xin Liu, Zhisong Pan

In this work, we propose FedSSO, a server-side second-order optimization method for federated learning (FL).

Federated Learning

Boosting Graph Structure Learning with Dummy Nodes

1 code implementation17 Jun 2022 Xin Liu, Jiayang Cheng, Yangqiu Song, Xin Jiang

We extend graph kernels and graph neural networks with dummy nodes and conduct experiments on graph classification and subgraph isomorphism matching tasks.

Graph Classification Graph Representation Learning +1

A Multi-task Framework for Infrared Small Target Detection and Segmentation

1 code implementation14 Jun 2022 Yuhang Chen, Liyuan Li, Xin Liu, Xiaofeng Su, Fansheng Chen

First, with the use of UNet as the backbone to maintain resolution and semantic information, our model can achieve a higher detection accuracy than other state-of-the-art methods by attaching a simple anchor-free head.

Multi-Task Learning object-detection +3

SCAMPS: Synthetics for Camera Measurement of Physiological Signals

2 code implementations8 Jun 2022 Daniel McDuff, Miah Wander, Xin Liu, Brian L. Hill, Javier Hernandez, Jonathan Lester, Tadas Baltrusaitis

The use of cameras and computational algorithms for noninvasive, low-cost and scalable measurement of physiological (e. g., cardiac and pulmonary) vital signs is very attractive.

Descriptive Heart Rate Variability

Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment

no code implementations26 May 2022 Zixian Yang, Xin Liu, Lei Ying

To understand the exploration, exploitation, and engagement in these systems, we propose a new model, called MAB-A where "A" stands for abandonment and the abandonment probability depends on the current recommended item and the user's past experience (called state).

Multi-Armed Bandits Q-Learning +1