Search Results for author: Jun Wang

Found 521 papers, 167 papers with code

Eureka: Neural Insight Learning for Knowledge Graph Reasoning

no code implementations COLING 2022 Alex X. Zhang, Xun Liang, Bo Wu, Xiangping Zheng, Sensen Zhang, Yuhui Guo, Jun Wang, Xinyao Liu

The human recognition system has presented the remarkable ability to effortlessly learn novel knowledge from only a few trigger events based on prior knowledge, which is called insight learning.

Few-Shot Learning

PA Ph&Tech at SemEval-2022 Task 11: NER Task with Ensemble Embedding from Reinforcement Learning

no code implementations SemEval (NAACL) 2022 Qizhi Lin, Changyu Hou, Xiaopeng Wang, Jun Wang, Yixuan Qiao, Peng Jiang, Xiandi Jiang, Benqi Wang, Qifeng Xiao

From pretrained contextual embedding to document-level embedding, the selection and construction of embedding have drawn more and more attention in the NER domain in recent research.

NER Zero-Shot Learning

Measuring and Mitigating Name Biases in Neural Machine Translation

no code implementations ACL 2022 Jun Wang, Benjamin Rubinstein, Trevor Cohn

In this paper we describe a new source of bias prevalent in NMT systems, relating to translations of sentences containing person names.

Data Augmentation Machine Translation +2

Detecting Health Advice in Medical Research Literature

1 code implementation EMNLP 2021 Yingya Li, Jun Wang, Bei Yu

We also conducted a case study that applied this prediction model to retrieve specific health advice on COVID-19 treatments from LitCovid, a large COVID research literature portal, demonstrating the usefulness of retrieving health advice sentences as an advanced research literature navigation function for health researchers and the general public.

Retrieval Sentence

数字人文视角下的《史记》《汉书》比较研究(A Comparative Study of Shiji and Hanshu from the Perspective of Digital Humanities)

no code implementations CCL 2022 Zekun Deng, Hao Yang, Jun Wang

"《史记》和《汉书》具有经久不衰的研究价值。尽管两书异同的研究已经较为丰富, 但研究的全面性、完备性、科学性、客观性均仍显不足。在数字人文的视角下, 本文利用计算语言学方法, 通过对字、词、命名实体、段落等的多粒度、多角度分析, 开展对于《史》《汉》的比较研究。首先, 本文对于《史》《汉》中的字、词、命名实体的分布和特点进行对比, 以遍历穷举的考察方式提炼出两书在主要内容上的相同点与不同点, 揭示了汉武帝之前和汉武帝到西汉灭亡两段历史时期在政治、文化、思想上的重要变革与承袭。其次, 本文使用一种融入命名实体作为外部特征的文本相似度算法对于《史记》《汉书》的异文进行自动发现, 成功识别出过去研究者通过人工手段没有发现的袭用段落, 使得我们对于《史》《汉》的承袭关系形成更加完整和立体的认识。再次, 本文通过计算异文段落之间的最长公共子序列来自动得出两段异文之间存在的差异, 从宏观统计上证明了《汉书》文字风格《史记》的差别, 并从微观上进一步对二者语言特点进行了阐释, 为理解《史》《汉》异文特点提供了新的角度和启发。本研究站在数字人文的视域下, 利用先进的计算方法对于传世千年的中国古代经典进行了再审视、再发现, 其方法对于今人研究古籍有一定的借鉴价值。”

ALISA: Accelerating Large Language Model Inference via Sparsity-Aware KV Caching

no code implementations26 Mar 2024 Youpeng Zhao, Di wu, Jun Wang

In a single GPU-CPU system, we demonstrate that under varying workloads, ALISA improves the throughput of baseline systems such as FlexGen and vLLM by up to 3X and 1. 9X, respectively.

Language Modelling Large Language Model +1

CHisIEC: An Information Extraction Corpus for Ancient Chinese History

no code implementations22 Mar 2024 Xuemei Tang, Zekun Deng, Qi Su, Hao Yang, Jun Wang

Additionally, we have evaluated the capabilities of Large Language Models (LLMs) in the context of tasks related to ancient Chinese history.

named-entity-recognition Named Entity Recognition +3

Privacy-Preserving Face Recognition Using Trainable Feature Subtraction

1 code implementation19 Mar 2024 Yuxi Mi, Zhizhou Zhong, Yuge Huang, Jiazhen Ji, Jianqing Xu, Jun Wang, Shaoming Wang, Shouhong Ding, Shuigeng Zhou

Recognizable identity features within the image are encouraged by co-training a recognition model on its high-dimensional feature representation.

Face Recognition Image Compression +1

Learning Macroeconomic Policies based on Microfoundations: A Stackelberg Mean Field Game Approach

no code implementations14 Mar 2024 Qirui Mi, Zhiyu Zhao, Siyu Xia, Yan Song, Jun Wang, Haifeng Zhang

Effective macroeconomic policies play a crucial role in promoting economic growth and social stability.

Circuit Transformer: End-to-end Circuit Design by Predicting the Next Gate

no code implementations14 Mar 2024 Xihan Li, Xing Li, Lei Chen, Xing Zhang, Mingxuan Yuan, Jun Wang

Then, can circuits also be mastered by a a sufficiently large "circuit model", which can conquer electronic design tasks by simply predicting the next logic gate?

Hallucination

Post-Training Attribute Unlearning in Recommender Systems

no code implementations11 Mar 2024 Chaochao Chen, Yizhao Zhang, Yuyuan Li, Dan Meng, Jun Wang, Xiaoli Zheng, Jianwei Yin

The first component is distinguishability loss, where we design a distribution-based measurement to make attribute labels indistinguishable from attackers.

Attribute Recommendation Systems

Segmentation Guided Sparse Transformer for Under-Display Camera Image Restoration

no code implementations9 Mar 2024 Jingyun Xue, Tao Wang, Jun Wang, Kaihao Zhang, Wenhan Luo, Wenqi Ren, Zikun Liu, Hyunhee Park, Xiaochun Cao

Specifically, we utilize sparse self-attention to filter out redundant information and noise, directing the model's attention to focus on the features more relevant to the degraded regions in need of reconstruction.

Image Restoration Instance Segmentation +1

Scene Graph Aided Radiology Report Generation

no code implementations8 Mar 2024 Jun Wang, Lixing Zhu, Abhir Bhalerao, Yulan He

Radiology report generation (RRG) methods often lack sufficient medical knowledge to produce clinically accurate reports.

Knowledge Distillation Sentence

Looking Ahead to Avoid Being Late: Solving Hard-Constrained Traveling Salesman Problem

no code implementations8 Mar 2024 Jingxiao Chen, Ziqin Gong, Minghuan Liu, Jun Wang, Yong Yu, Weinan Zhang

To overcome this problem and to have an effective solution against hard constraints, we proposed a novel learning-based method that uses looking-ahead information as the feature to improve the legality of TSP with Time Windows (TSPTW) solutions.

Traveling Salesman Problem

A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods

no code implementations5 Mar 2024 Hanlei Jin, Yang Zhang, Dan Meng, Jun Wang, Jinghua Tan

Automatic Text Summarization (ATS), utilizing Natural Language Processing (NLP) algorithms, aims to create concise and accurate summaries, thereby significantly reducing the human effort required in processing large volumes of text.

Text Summarization

Merino: Entropy-driven Design for Generative Language Models on IoT Devices

no code implementations28 Feb 2024 Youpeng Zhao, Ming Lin, Huadong Tang, Qiang Wu, Jun Wang

Generative Large Language Models (LLMs) stand as a revolutionary advancement in the modern era of artificial intelligence (AI).

DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning

1 code implementation27 Feb 2024 Siyuan Guo, Cheng Deng, Ying Wen, Hechang Chen, Yi Chang, Jun Wang

In the development stage, DS-Agent follows the CBR framework to structure an automatic iteration pipeline, which can flexibly capitalize on the expert knowledge from Kaggle, and facilitate consistent performance improvement through the feedback mechanism.

Code Generation

GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series

no code implementations26 Feb 2024 Chengzhe Piao, Taiyu Zhu, Stephanie E Baldeweg, Paul Taylor, Pantelis Georgiou, Jiahao Sun, Jun Wang, Kezhi Li

Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with diabetes, thereby reducing complications and improving quality of life.

Graph Attention Management +1

Safe Task Planning for Language-Instructed Multi-Robot Systems using Conformal Prediction

no code implementations23 Feb 2024 Jun Wang, Guocheng He, Yiannis Kantaros

Several recent works have addressed similar planning problems by leveraging pre-trained Large Language Models (LLMs) to design effective multi-robot plans.

Conformal Prediction Uncertainty Quantification

CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation

no code implementations22 Feb 2024 Jun Wang, Yuzhe Qin, Kaiming Kuang, Yigit Korkmaz, Akhilan Gurumoorthy, Hao Su, Xiaolong Wang

We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks.

Data Augmentation Imitation Learning

Small Language Model Is a Good Guide for Large Language Model in Chinese Entity Relation Extraction

no code implementations22 Feb 2024 Xuemei Tang, Jun Wang, Qi Su

Recently, large language models (LLMs) have been successful in relational extraction (RE) tasks, especially in the few-shot learning.

Few-Shot Learning Language Modelling +3

Bayesian Reward Models for LLM Alignment

no code implementations20 Feb 2024 Adam X. Yang, Maxime Robeyns, Thomas Coste, Jun Wang, Haitham Bou-Ammar, Laurence Aitchison

To ensure that large language model (LLM) responses are helpful and non-toxic, we usually fine-tune a reward model on human preference data.

Language Modelling Large Language Model

Case Study: Testing Model Capabilities in Some Reasoning Tasks

no code implementations15 Feb 2024 Min Zhang, Sato Takumi, Jack Zhang, Jun Wang

Large Language Models (LLMs) excel in generating personalized content and facilitating interactive dialogues, showcasing their remarkable aptitude for a myriad of applications.

Intelligent Agricultural Management Considering N$_2$O Emission and Climate Variability with Uncertainties

no code implementations13 Feb 2024 Zhaoan Wang, Shaoping Xiao, Jun Wang, Ashwin Parab, Shivam Patel

This study examines how artificial intelligence (AI), especially Reinforcement Learning (RL), can be used in farming to boost crop yields, fine-tune nitrogen use and watering, and reduce nitrate runoff and greenhouse gases, focusing on Nitrous Oxide (N$_2$O) emissions from soil.

Decision Making Management +2

Natural Language Reinforcement Learning

no code implementations11 Feb 2024 Xidong Feng, Ziyu Wan, Mengyue Yang, Ziyan Wang, Girish A. Koushik, Yali Du, Ying Wen, Jun Wang

Reinforcement Learning (RL) has shown remarkable abilities in learning policies for decision-making tasks.

Decision Making reinforcement-learning +1

Entropy-Regularized Token-Level Policy Optimization for Large Language Models

1 code implementation9 Feb 2024 Muning Wen, Cheng Deng, Jun Wang, Weinan Zhang, Ying Wen

At the heart of ETPO is our novel per-token soft Bellman update, designed to harmonize the RL process with the principles of language modeling.

Code Generation Decision Making +3

A Statistical Model of Bursty Mixed Gaussian-impulsive Noise: Model and Parameter Estimation

no code implementations9 Feb 2024 Tianfu Qi, Jun Wang

In the first part, we propose a closed-form heavy-tailed multivariate probability density function (PDF) that to model the bursty mixed noise.

Understanding the Role of Cross-Entropy Loss in Fairly Evaluating Large Language Model-based Recommendation

no code implementations9 Feb 2024 Cong Xu, Zhangchi Zhu, Jun Wang, Jianyong Wang, Wei zhang

Large language models (LLMs) have gained much attention in the recommendation community; some studies have observed that LLMs, fine-tuned by the cross-entropy loss with a full softmax, could achieve state-of-the-art performance already.

Language Modelling Large Language Model

CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation

no code implementations8 Feb 2024 Jun Wang, Haoxuan Li, Chi Zhang, Dongxu Liang, Enyun Yu, Wenwu Ou, Wenjia Wang

Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCVR) prediction.

Contrastive Learning counterfactual +3

InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information Minimization

no code implementations23 Jan 2024 Jiarui Jin, Zexue He, Mengyue Yang, Weinan Zhang, Yong Yu, Jun Wang, Julian McAuley

Subsequently, we minimize the mutual information between the observation estimation and the relevance estimation conditioned on the input features.

Learning-To-Rank Recommendation Systems

A Framework to Implement 1+N Multi-task Fine-tuning Pattern in LLMs Using the CGC-LORA Algorithm

no code implementations22 Jan 2024 Chao Song, Zhihao Ye, Qiqiang Lin, Qiuying Peng, Jun Wang

In practice, there are two prevailing ways, in which the adaptation can be achieved: (i) Multiple Independent Models: Pre-trained LLMs are fine-tuned a few times independently using the corresponding training samples from each task.

Skeleton-Guided Instance Separation for Fine-Grained Segmentation in Microscopy

no code implementations18 Jan 2024 Jun Wang, Chengfeng Zhou, Zhaoyan Ming, Lina Wei, Xudong Jiang, Dahong Qian

One of the fundamental challenges in microscopy (MS) image analysis is instance segmentation (IS), particularly when segmenting cluster regions where multiple objects of varying sizes and shapes may be connected or even overlapped in arbitrary orientations.

Instance Segmentation Semantic Segmentation

Large Language Models Are Neurosymbolic Reasoners

1 code implementation17 Jan 2024 Meng Fang, Shilong Deng, Yudi Zhang, Zijing Shi, Ling Chen, Mykola Pechenizkiy, Jun Wang

A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning.

Common Sense Reasoning Math +2

Adversarial Masking Contrastive Learning for vein recognition

no code implementations16 Jan 2024 Huafeng Qin, Yiquan Wu, Mounim A. El-Yacoubi, Jun Wang, Guangxiang Yang

To overcome this problem, in this paper, we propose an adversarial masking contrastive learning (AMCL) approach, that generates challenging samples to train a more robust contrastive learning model for the downstream palm-vein recognition task, by alternatively optimizing the encoder in the contrastive learning model and a set of latent variables.

Contrastive Learning Generative Adversarial Network

Can AI Write Classical Chinese Poetry like Humans? An Empirical Study Inspired by Turing Test

no code implementations10 Jan 2024 Zekun Deng, Hao Yang, Jun Wang

Some argue that the essence of humanity, such as creativity and sentiment, can never be mimicked by machines.

Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives

no code implementations4 Jan 2024 Wenqi Zhang, Yongliang Shen, Linjuan Wu, Qiuying Peng, Jun Wang, Yueting Zhuang, Weiming Lu

Experiments conducted on a series of reasoning and translation tasks with different LLMs serve to underscore the effectiveness and generality of our strategy.

Language Modelling Large Language Model

Learning-based agricultural management in partially observable environments subject to climate variability

no code implementations2 Jan 2024 Zhaoan Wang, Shaoping Xiao, Junchao Li, Jun Wang

However, our study illuminates the need for agent retraining to acquire new optimal policies under extreme weather events.

Management

GazeCLIP: Towards Enhancing Gaze Estimation via Text Guidance

no code implementations30 Dec 2023 Jun Wang, Hao Ruan, Mingjie Wang, Chuanghui Zhang, Huachun Li, Jun Zhou

Over the past decade, visual gaze estimation has garnered growing attention within the research community, thanks to its wide-ranging application scenarios.

Gaze Estimation Image Generation

Maximum Likelihood CFO Estimation for High-Mobility OFDM Systems: A Chinese Remainder Theorem Based Method

no code implementations27 Dec 2023 Wei Huang, Jun Wang, Xiaoping Li, Qihang Peng

Orthogonal frequency division multiplexing (OFDM) is a widely adopted wireless communication technique but is sensitive to the carrier frequency offset (CFO).

Enhanced Latent Multi-view Subspace Clustering

1 code implementation22 Dec 2023 Long Shi, Lei Cao, Jun Wang, Badong Chen

Specifically, we stack the data matrices from various views into the block-diagonal locations of the augmented matrix to exploit the complementary information.

Clustering Multi-view Subspace Clustering

Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling

no code implementations21 Dec 2023 Jie Han, Yixiong Zou, Haozhao Wang, Jun Wang, Wei Liu, Yao Wu, Tao Zhang, Ruixuan Li

Therefore, current works first train a model on source domains with sufficiently labeled data, and then transfer the model to target domains where only rarely labeled data is available.

intent-classification Intent Classification +4

Multi-stages attention Breast cancer classification based on nonlinear spiking neural P neurons with autapses

no code implementations20 Dec 2023 Bo Yang, Hong Peng, Xiaohui Luo, Jun Wang

Downsampling in deep networks may lead to loss of information, so for compensating the detail and edge information and allowing convolutional neural networks to pay more attention to seek the lesion region, we propose a multi-stages attention architecture based on NSNP neurons with autapses.

Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach

1 code implementation19 Dec 2023 Weiyu Ma, Qirui Mi, Xue Yan, Yuqiao Wu, Runji Lin, Haifeng Zhang, Jun Wang

StarCraft II is a challenging benchmark for AI agents due to the necessity of both precise micro level operations and strategic macro awareness.

Language Modelling Large Language Model +2

DMT: Comprehensive Distillation with Multiple Self-supervised Teachers

no code implementations19 Dec 2023 Yuang Liu, Jing Wang, Qiang Zhou, Fan Wang, Jun Wang, Wei zhang

Numerous self-supervised learning paradigms, such as contrastive learning and masked image modeling, have been proposed to acquire powerful and general representations from unlabeled data.

Contrastive Learning Model Compression +1

A survey on algorithms for Nash equilibria in finite normal-form games

no code implementations18 Dec 2023 Hanyu Li, Wenhan Huang, Zhijian Duan, David Henry Mguni, Kun Shao, Jun Wang, Xiaotie Deng

This paper reviews various algorithms computing the Nash equilibrium and its approximation solutions in finite normal-form games from both theoretical and empirical perspectives.

Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis

no code implementations18 Dec 2023 Rohan Mitta, Hosein Hasanbeig, Jun Wang, Daniel Kroening, Yiannis Kantaros, Alessandro Abate

This paper addresses the problem of maintaining safety during training in Reinforcement Learning (RL), such that the safety constraint violations are bounded at any point during learning.

Bayesian Inference Reinforcement Learning (RL)

Supervised Contrastive Learning for Fine-grained Chromosome Recognition

no code implementations12 Dec 2023 Ruijia Chang, Suncheng Xiang, Chengyu Zhou, Kui Su, Dahong Qian, Jun Wang

Chromosome recognition is an essential task in karyotyping, which plays a vital role in birth defect diagnosis and biomedical research.

Contrastive Learning

Multi-granularity Causal Structure Learning

no code implementations9 Dec 2023 Jiaxuan Liang, Jun Wang, Guoxian Yu, Shuyin Xia, Guoyin Wang

Unveil, model, and comprehend the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines.

Federated Causality Learning with Explainable Adaptive Optimization

no code implementations9 Dec 2023 Dezhi Yang, Xintong He, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Jinglin Zhang

We design a global optimization formula to naturally aggregate the causal graphs from client data and constrain the acyclicity of the global graph without exposing local data.

Causal Discovery

Multi-dimensional Fair Federated Learning

no code implementations9 Dec 2023 Cong Su, Guoxian Yu, Jun Wang, Hui Li, Qingzhong Li, Han Yu

Federated learning (FL) has emerged as a promising collaborative and secure paradigm for training a model from decentralized data without compromising privacy.

Fairness Federated Learning

Enhancing the Rationale-Input Alignment for Self-explaining Rationalization

no code implementations7 Dec 2023 Wei Liu, Haozhao Wang, Jun Wang, Zhiying Deng, Yuankai Zhang, Cheng Wang, Ruixuan Li

Rationalization empowers deep learning models with self-explaining capabilities through a cooperative game, where a generator selects a semantically consistent subset of the input as a rationale, and a subsequent predictor makes predictions based on the selected rationale.

Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters

1 code implementation28 Nov 2023 Dayu Hu, Zhibin Dong, Ke Liang, Jun Wang, Siwei Wang, Xinwang Liu

To this end, we introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data, which seamlessly integrates information from different views without the need for a predefined number of clusters.

Clustering Community Detection

Verified Compositional Neuro-Symbolic Control for Stochastic Systems with Temporal Logic Tasks

no code implementations17 Nov 2023 Jun Wang, Haojun Chen, Zihe Sun, Yiannis Kantaros

To the best of our knowledge, this is the first work that designs verified temporal compositions of NN controllers for unknown and stochastic systems.

Robot Navigation

Ask more, know better: Reinforce-Learned Prompt Questions for Decision Making with Large Language Models

no code implementations27 Oct 2023 Xue Yan, Yan Song, Xinyu Cui, Filippos Christianos, Haifeng Zhang, David Henry Mguni, Jun Wang

To that purpose, we offer a new leader-follower bilevel framework that is capable of learning to ask relevant questions (prompts) and subsequently undertaking reasoning to guide the learning of actions.

Decision Making

Specify Robust Causal Representation from Mixed Observations

1 code implementation21 Oct 2023 Mengyue Yang, Xinyu Cai, Furui Liu, Weinan Zhang, Jun Wang

Under the hypothesis that the intrinsic latent factors follow some casual generative models, we argue that by learning a causal representation, which is the minimal sufficient causes of the whole system, we can improve the robustness and generalization performance of machine learning models.

Why Can Large Language Models Generate Correct Chain-of-Thoughts?

no code implementations20 Oct 2023 Rasul Tutunov, Antoine Grosnit, Juliusz Ziomek, Jun Wang, Haitham Bou-Ammar

This paper delves into the capabilities of large language models (LLMs), specifically focusing on advancing the theoretical comprehension of chain-of-thought prompting.

Text Generation

FusionU-Net: U-Net with Enhanced Skip Connection for Pathology Image Segmentation

1 code implementation17 Oct 2023 Zongyi Li, Hongbing Lyu, Jun Wang

One of the key designs of U-Net is the use of skip connections between the encoder and decoder, which helps to recover detailed information after upsampling.

Image Segmentation Semantic Segmentation

How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances

1 code implementation11 Oct 2023 Zihan Zhang, Meng Fang, Ling Chen, Mohammad-Reza Namazi-Rad, Jun Wang

Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment.

World Knowledge

GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models

1 code implementation8 Oct 2023 Hanjing Wang, Man-Kit Sit, Congjie He, Ying Wen, Weinan Zhang, Jun Wang, Yaodong Yang, Luo Mai

This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed to perform scalable reinforcement learning (RL) with large sequence models (such as transformers).

Reinforcement Learning (RL)

Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems

no code implementations6 Oct 2023 Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Yizhao Zhang, Zhongxuan Han, Dan Meng, Jun Wang

To address the PoT-AU problem in recommender systems, we design a two-component loss function that consists of i) distinguishability loss: making attribute labels indistinguishable from attackers, and ii) regularization loss: preventing drastic changes in the model that result in a negative impact on recommendation performance.

Attribute Recommendation Systems

Realistic Speech-to-Face Generation with Speech-Conditioned Latent Diffusion Model with Face Prior

no code implementations5 Oct 2023 Jinting Wang, Li Liu, Jun Wang, Hei Victor Cheng

To overcome this challenge, we introduce the concept of residuals by integrating a statistical face prior to the diffusion process.

Face Generation

Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

1 code implementation29 Sep 2023 Xidong Feng, Ziyu Wan, Muning Wen, Stephen Marcus McAleer, Ying Wen, Weinan Zhang, Jun Wang

Empirical results across reasoning, planning, alignment, and decision-making tasks show that TS-LLM outperforms existing approaches and can handle trees with a depth of 64.

Decision Making Language Modelling +1

Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution

no code implementations24 Sep 2023 Cong Xu, Jun Wang, Jianyong Wang, Wei zhang

Embedding plays a critical role in modern recommender systems because they are virtual representations of real-world entities and the foundation for subsequent decision models.

Recommendation Systems

D-Separation for Causal Self-Explanation

1 code implementation NeurIPS 2023 Wei Liu, Jun Wang, Haozhao Wang, Ruixuan Li, Zhiying Deng, Yuankai Zhang, Yang Qiu

Instead of attempting to rectify the issues of the MMI criterion, we propose a novel criterion to uncover the causal rationale, termed the Minimum Conditional Dependence (MCD) criterion, which is grounded on our finding that the non-causal features and the target label are \emph{d-separated} by the causal rationale.

Invariant Learning via Probability of Sufficient and Necessary Causes

1 code implementation NeurIPS 2023 Mengyue Yang, Zhen Fang, Yonggang Zhang, Yali Du, Furui Liu, Jean-Francois Ton, Jianhong Wang, Jun Wang

To capture the information of sufficient and necessary causes, we employ a classical concept, the probability of sufficiency and necessary causes (PNS), which indicates the probability of whether one is the necessary and sufficient cause.

MiChao-HuaFen 1.0: A Specialized Pre-trained Corpus Dataset for Domain-specific Large Models

no code implementations21 Sep 2023 Yidong Liu, FuKai Shang, Fang Wang, Rui Xu, Jun Wang, Wei Li, Yao Li, Conghui He

With the advancement of deep learning technologies, general-purpose large models such as GPT-4 have demonstrated exceptional capabilities across various domains.

Conformal Temporal Logic Planning using Large Language Models

no code implementations18 Sep 2023 Jun Wang, Jiaming Tong, Kaiyuan Tan, Yevgeniy Vorobeychik, Yiannis Kantaros

To formally define the overarching mission, we leverage Linear Temporal Logic (LTL) defined over atomic predicates modeling these NL-based sub-tasks.

Conformal Prediction Motion Planning

Cross-Utterance Conditioned VAE for Speech Generation

no code implementations8 Sep 2023 Yang Li, Cheng Yu, Guangzhi Sun, Weiqin Zu, Zheng Tian, Ying Wen, Wei Pan, Chao Zhang, Jun Wang, Yang Yang, Fanglei Sun

Experimental results on the LibriTTS datasets demonstrate that our proposed models significantly enhance speech synthesis and editing, producing more natural and expressive speech.

Speech Synthesis

In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems

no code implementations4 Sep 2023 Zhongxuan Han, Chaochao Chen, Xiaolin Zheng, Weiming Liu, Jun Wang, Wenjie Cheng, Yuyuan Li

By combining the fairness loss with the original backbone model loss, we address the UOF issue and maintain the overall recommendation performance simultaneously.

Fairness Recommendation Systems

Can Prompt Learning Benefit Radiology Report Generation?

no code implementations30 Aug 2023 Jun Wang, Lixing Zhu, Abhir Bhalerao, Yulan He

Radiology report generation aims to automatically provide clinically meaningful descriptions of radiology images such as MRI and X-ray.

Image Captioning Prompt Engineering

Enhancing In-Situ Structural Health Monitoring through RF Energy-Powered Sensor Nodes and Mobile Platform

no code implementations20 Aug 2023 Yu Luo, Lina Pu, Jun Wang, Isaac Howard

The experimental results indicate that an active RF-SN embedded in concrete at a depth of 13. 5 cm can be effectively powered by a 915MHz mobile radio transmitter with an effective isotropic radiated power (EIRP) of 32. 5dBm.

JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games

no code implementations9 Aug 2023 Yang Li, Kun Xiong, Yingping Zhang, Jiangcheng Zhu, Stephen Mcaleer, Wei Pan, Jun Wang, Zonghong Dai, Yaodong Yang

This paper presents an empirical exploration of non-transitivity in perfect-information games, specifically focusing on Xiangqi, a traditional Chinese board game comparable in game-tree complexity to chess and shogi.

Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank

no code implementations5 Aug 2023 Jiarui Jin, Xianyu Chen, Weinan Zhang, Mengyue Yang, Yang Wang, Yali Du, Yong Yu, Jun Wang

Notice that these ranking metrics do not consider the effects of the contextual dependence among the items in the list, we design a new family of simulation-based ranking metrics, where existing metrics can be regarded as special cases.

Learning-To-Rank

Dynamic Token-Pass Transformers for Semantic Segmentation

no code implementations3 Aug 2023 Yuang Liu, Qiang Zhou, Jing Wang, Fan Wang, Jun Wang, Wei zhang

Vision transformers (ViT) usually extract features via forwarding all the tokens in the self-attention layers from top to toe.

Segmentation Semantic Segmentation

HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning

no code implementations26 Jul 2023 Xinting Liao, Weiming Liu, Chaochao Chen, Pengyang Zhou, Huabin Zhu, Yanchao Tan, Jun Wang, Yue Qi

Firstly, HPTI in the server constructs uniformly distributed and fixed class prototypes, and shares them with clients to match class statistics, further guiding consistent feature representation for local clients.

Federated Learning

A Siamese-based Verification System for Open-set Architecture Attribution of Synthetic Images

1 code implementation19 Jul 2023 Lydia Abady, Jun Wang, Benedetta Tondi, Mauro Barni

In the second setting, the system verifies a claim about the architecture used to generate a synthetic image, utilizing one or multiple reference images generated by the claimed architecture.

Attribute Image Generation +1

Multi-Scale Prototypical Transformer for Whole Slide Image Classification

no code implementations5 Jul 2023 Saisai Ding, Jun Wang, Juncheng Li, Jun Shi

The PT is developed to reduce redundant instances in bags by integrating prototypical learning into the Transformer architecture.

Classification Image Classification +1

Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach

no code implementations3 Jul 2023 Shuo Chen, Ning Yang, Meng Zhang, Jun Wang

In this paper, we consider multiple users offloading tasks to heterogeneous edge servers in a MEC system.

Edge-computing

Large Sequence Models for Sequential Decision-Making: A Survey

no code implementations24 Jun 2023 Muning Wen, Runji Lin, Hanjing Wang, Yaodong Yang, Ying Wen, Luo Mai, Jun Wang, Haifeng Zhang, Weinan Zhang

Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e. g., GPT-3 and Swin Transformer.

Decision Making

ChessGPT: Bridging Policy Learning and Language Modeling

1 code implementation NeurIPS 2023 Xidong Feng, Yicheng Luo, Ziyan Wang, Hongrui Tang, Mengyue Yang, Kun Shao, David Mguni, Yali Du, Jun Wang

Thus, we propose ChessGPT, a GPT model bridging policy learning and language modeling by integrating data from these two sources in Chess games.

Decision Making Language Modelling

Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers with Partially Annotated Ultrasound Images

no code implementations12 Jun 2023 Jian Wang, Liang Qiao, Shichong Zhou, Jin Zhou, Jun Wang, Juncheng Li, Shihui Ying, Cai Chang, Jun Shi

To address this issue, a novel Two-Stage Detection and Diagnosis Network (TSDDNet) is proposed based on weakly supervised learning to enhance diagnostic accuracy of the ultrasound-based CAD for breast cancers.

Lesion Detection Weakly-supervised Learning

Negotiated Reasoning: On Provably Addressing Relative Over-Generalization

no code implementations8 Jun 2023 Junjie Sheng, Wenhao Li, Bo Jin, Hongyuan Zha, Jun Wang, Xiangfeng Wang

Recent methods have shown that assigning reasoning ability to agents can mitigate RO algorithmically and empirically, but there has been a lack of theoretical understanding of RO, let alone designing provably RO-free methods.

Multi-agent Reinforcement Learning

XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations

1 code implementation7 Jun 2023 Yusen Zhang, Jun Wang, Zhiguo Wang, Rui Zhang

However, existing CLSP models are separately proposed and evaluated on datasets of limited tasks and applications, impeding a comprehensive and unified evaluation of CLSP on a diverse range of NLs and MRs. To this end, we present XSemPLR, a unified benchmark for cross-lingual semantic parsing featured with 22 natural languages and 8 meaning representations by examining and selecting 9 existing datasets to cover 5 tasks and 164 domains.

Cross-Lingual Transfer Semantic Parsing +2

User Behavior Simulation with Large Language Model based Agents

1 code implementation5 Jun 2023 Lei Wang, Jingsen Zhang, Hao Yang, ZhiYuan Chen, Jiakai Tang, Zeyu Zhang, Xu Chen, Yankai Lin, Ruihua Song, Wayne Xin Zhao, Jun Xu, Zhicheng Dou, Jun Wang, Ji-Rong Wen

Simulating high quality user behavior data has always been a fundamental problem in human-centered applications, where the major difficulty originates from the intricate mechanism of human decision process.

Language Modelling Large Language Model +2

Incorporating Deep Syntactic and Semantic Knowledge for Chinese Sequence Labeling with GCN

no code implementations3 Jun 2023 Xuemei Tang, Jun Wang, Qi Su

Recently, it is quite common to integrate Chinese sequence labeling results to enhance syntactic and semantic parsing.

Chinese Word Segmentation Part-Of-Speech Tagging +1

Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach

no code implementations NeurIPS 2023 Yudi Zhang, Yali Du, Biwei Huang, Ziyan Wang, Jun Wang, Meng Fang, Mykola Pechenizkiy

While the majority of current approaches construct the reward redistribution in an uninterpretable manner, we propose to explicitly model the contributions of state and action from a causal perspective, resulting in an interpretable reward redistribution and preserving policy invariance.

reinforcement-learning

UNITE: A Unified Benchmark for Text-to-SQL Evaluation

1 code implementation25 May 2023 Wuwei Lan, Zhiguo Wang, Anuj Chauhan, Henghui Zhu, Alexander Li, Jiang Guo, Sheng Zhang, Chung-Wei Hang, Joseph Lilien, Yiqun Hu, Lin Pan, Mingwen Dong, Jun Wang, Jiarong Jiang, Stephen Ash, Vittorio Castelli, Patrick Ng, Bing Xiang

A practical text-to-SQL system should generalize well on a wide variety of natural language questions, unseen database schemas, and novel SQL query structures.

Text-To-SQL

IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks

1 code implementation25 May 2023 Xuanli He, Jun Wang, Benjamin Rubinstein, Trevor Cohn

Backdoor attacks are an insidious security threat against machine learning models.

Multi-scale Efficient Graph-Transformer for Whole Slide Image Classification

no code implementations25 May 2023 Saisai Ding, Juncheng Li, Jun Wang, Shihui Ying, Jun Shi

The key idea of MEGT is to adopt two independent Efficient Graph-based Transformer (EGT) branches to process the low-resolution and high-resolution patch embeddings (i. e., tokens in a Transformer) of WSIs, respectively, and then fuse these tokens via a multi-scale feature fusion module (MFFM).

Image Classification whole slide images

Collaborative Recommendation Model Based on Multi-modal Multi-view Attention Network: Movie and literature cases

no code implementations24 May 2023 Zheng Hu, Shi-Min Cai, Jun Wang, Tao Zhou

Thus, the representation of users' dislikes should be integrated into the user modelling when we construct a collaborative recommendation model.

Decoupled Rationalization with Asymmetric Learning Rates: A Flexible Lipschitz Restraint

1 code implementation23 May 2023 Wei Liu, Jun Wang, Haozhao Wang, Ruixuan Li, Yang Qiu, Yuankai Zhang, Jie Han, Yixiong Zou

However, such a cooperative game may incur the degeneration problem where the predictor overfits to the uninformative pieces generated by a not yet well-trained generator and in turn, leads the generator to converge to a sub-optimal model that tends to select senseless pieces.

Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation

1 code implementation19 May 2023 Xuanli He, Qiongkai Xu, Jun Wang, Benjamin Rubinstein, Trevor Cohn

Modern NLP models are often trained over large untrusted datasets, raising the potential for a malicious adversary to compromise model behaviour.

An Empirical Study on Google Research Football Multi-agent Scenarios

1 code implementation16 May 2023 Yan Song, He Jiang, Zheng Tian, Haifeng Zhang, Yingping Zhang, Jiangcheng Zhu, Zonghong Dai, Weinan Zhang, Jun Wang

Few multi-agent reinforcement learning (MARL) research on Google Research Football (GRF) focus on the 11v11 multi-agent full-game scenario and to the best of our knowledge, no open benchmark on this scenario has been released to the public.

Benchmarking Multi-agent Reinforcement Learning +1

MGR: Multi-generator Based Rationalization

1 code implementation8 May 2023 Wei Liu, Haozhao Wang, Jun Wang, Ruixuan Li, Xinyang Li, Yuankai Zhang, Yang Qiu

Rationalization is to employ a generator and a predictor to construct a self-explaining NLP model in which the generator selects a subset of human-intelligible pieces of the input text to the following predictor.

Leaf Cultivar Identification via Prototype-enhanced Learning

no code implementations5 May 2023 Yiyi Zhang, Zhiwen Ying, Ying Zheng, Cuiling Wu, Nannan Li, Jun Wang, Xianzhong Feng, Xiaogang Xu

Plant leaf identification is crucial for biodiversity protection and conservation and has gradually attracted the attention of academia in recent years.

Fine-Grained Image Classification

Structure Diagram Recognition in Financial Announcements

no code implementations26 Apr 2023 Meixuan Qiao, Jun Wang, Junfu Xiang, Qiyu Hou, Ruixuan Li

Accurately extracting structured data from structure diagrams in financial announcements is of great practical importance for building financial knowledge graphs and further improving the efficiency of various financial applications.

Knowledge Graphs

Filter Pruning via Filters Similarity in Consecutive Layers

no code implementations26 Apr 2023 Xiaorui Wang, Jun Wang, Xin Tang, Peng Gao, Rui Fang, Guotong Xie

Filter pruning is widely adopted to compress and accelerate the Convolutional Neural Networks (CNNs), but most previous works ignore the relationship between filters and channels in different layers.

Selective and Collaborative Influence Function for Efficient Recommendation Unlearning

no code implementations20 Apr 2023 Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Yizhao Zhang, Biao Gong, Jun Wang

In this paper, we first identify two main disadvantages of directly applying existing unlearning methods in the context of recommendation, i. e., (i) unsatisfactory efficiency for large-scale recommendation models and (ii) destruction of collaboration across users and items.

Recommendation Systems

Open Set Classification of GAN-based Image Manipulations via a ViT-based Hybrid Architecture

no code implementations11 Apr 2023 Jun Wang, Omran Alamayreh, Benedetta Tondi, Mauro Barni

Classification of AI-manipulated content is receiving great attention, for distinguishing different types of manipulations.

Attribute Classification +2

AutoKary2022: A Large-Scale Densely Annotated Dataset for Chromosome Instance Segmentation

no code implementations28 Mar 2023 Dan You, Pengcheng Xia, Qiuzhu Chen, Minghui Wu, Suncheng Xiang, Jun Wang

Automated chromosome instance segmentation from metaphase cell microscopic images is critical for the diagnosis of chromosomal disorders (i. e., karyotype analysis).

Instance Segmentation Segmentation +1

DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena

no code implementations12 Mar 2023 Jun Wang, Klaus Mueller

Furthermore, since an effect can be a cause of other effects, we allow users to aggregate different temporal cause-effect relations found with our method into a visual flow diagram to enable the discovery of temporal causal networks.

Time Series Analysis

DistilPose: Tokenized Pose Regression with Heatmap Distillation

1 code implementation CVPR 2023 Suhang Ye, Yingyi Zhang, Jie Hu, Liujuan Cao, Shengchuan Zhang, Lei Shen, Jun Wang, Shouhong Ding, Rongrong Ji

Specifically, DistilPose maximizes the transfer of knowledge from the teacher model (heatmap-based) to the student model (regression-based) through Token-distilling Encoder (TDE) and Simulated Heatmaps.

Knowledge Distillation Pose Estimation +1

Order Matters: Agent-by-agent Policy Optimization

no code implementations13 Feb 2023 Xihuai Wang, Zheng Tian, Ziyu Wan, Ying Wen, Jun Wang, Weinan Zhang

In this paper, we propose the \textbf{A}gent-by-\textbf{a}gent \textbf{P}olicy \textbf{O}ptimization (A2PO) algorithm to improve the sample efficiency and retain the guarantees of monotonic improvement for each agent during training.

Building Intelligence in the Mechanical Domain -- Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Tasks

no code implementations10 Feb 2023 Jun Wang, Suyi Li

In this paper, we experimentally examine the cognitive capability of a simple, paper-based Miura-ori -- using the physical reservoir computing framework -- to achieve different information perception tasks.

Stability Constrained OPF in Microgrids: A Chance Constrained Optimization Framework with Non-Gaussian Uncertainty

no code implementations4 Feb 2023 Jun Wang, Yue Song, David John Hill, Yunhe Hou, Feilong Fan

To figure out the stability issues brought by renewable energy sources (RES) with non-Gaussian uncertainties in isolated microgrids, this paper proposes a chance constrained stability constrained optimal power flow (CC-SC-OPF) model.

Benchmarking

Communication under Mixed Gaussian-Impulsive Channel: An End-to-End Framework

no code implementations19 Jan 2023 Chengjie Zhao, Jun Wang, Wei Huang, Xiaonan Chen, Tianfu Qi

Under MGIN channel, classical communication signal schemes and corresponding detection methods usually can not achieve desirable performance as they are optimized with respect to WGN.

PECAN: Leveraging Policy Ensemble for Context-Aware Zero-Shot Human-AI Coordination

1 code implementation16 Jan 2023 Xingzhou Lou, Jiaxian Guo, Junge Zhang, Jun Wang, Kaiqi Huang, Yali Du

We conduct experiments on the Overcooked environment, and evaluate the zero-shot human-AI coordination performance of our method with both behavior-cloned human proxies and real humans.

TopoSeg: Topology-Aware Nuclear Instance Segmentation

no code implementations ICCV 2023 Hongliang He, Jun Wang, Pengxu Wei, Fan Xu, Xiangyang Ji, Chang Liu, Jie Chen

Experiments on three nuclear instance segmentation datasets justify the superiority of TopoSeg, which achieves state-of-the-art performance.

Instance Segmentation Segmentation +1

Data-free Knowledge Distillation for Fine-grained Visual Categorization

1 code implementation ICCV 2023 Renrong Shao, Wei zhang, Jianhua Yin, Jun Wang

Our approach utilizes an adversarial distillation framework with attention generator, mixed high-order attention distillation, and semantic feature contrast learning.

Data-free Knowledge Distillation Fine-Grained Visual Categorization +1

On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective

1 code implementation24 Dec 2022 Ying Wen, Ziyu Wan, Ming Zhou, Shufang Hou, Zhe Cao, Chenyang Le, Jingxiao Chen, Zheng Tian, Weinan Zhang, Jun Wang

The pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making (IDM) systems.

Decision Making Image Captioning +2

Importance of Synthesizing High-quality Data for Text-to-SQL Parsing

no code implementations17 Dec 2022 Yiyun Zhao, Jiarong Jiang, Yiqun Hu, Wuwei Lan, Henry Zhu, Anuj Chauhan, Alexander Li, Lin Pan, Jun Wang, Chung-Wei Hang, Sheng Zhang, Marvin Dong, Joe Lilien, Patrick Ng, Zhiguo Wang, Vittorio Castelli, Bing Xiang

In this paper, we first examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further improve on popular benchmarks when trained with augmented synthetic data.

SQL Parsing SQL-to-Text +2

Sim-to-Real Transfer for Quadrupedal Locomotion via Terrain Transformer

no code implementations15 Dec 2022 Hang Lai, Weinan Zhang, Xialin He, Chen Yu, Zheng Tian, Yong Yu, Jun Wang

Deep reinforcement learning has recently emerged as an appealing alternative for legged locomotion over multiple terrains by training a policy in physical simulation and then transferring it to the real world (i. e., sim-to-real transfer).

Decision Making

TriNet: stabilizing self-supervised learning from complete or slow collapse on ASR

no code implementations12 Dec 2022 Lixin Cao, Jun Wang, Ben Yang, Dan Su, Dong Yu

Self-supervised learning (SSL) models confront challenges of abrupt informational collapse or slow dimensional collapse.

Self-Supervised Learning

Targeted Adversarial Attacks against Neural Network Trajectory Predictors

no code implementations8 Dec 2022 Kaiyuan Tan, Jun Wang, Yiannis Kantaros

To bridge this gap, in this paper, we propose a targeted adversarial attack against DNN models for trajectory forecasting tasks.

Adversarial Attack Trajectory Forecasting

WAIR-D: Wireless AI Research Dataset

no code implementations5 Dec 2022 Yourui Huangfu, Jian Wang, Shengchen Dai, Rong Li, Jun Wang, Chongwen Huang, Zhaoyang Zhang

The statistical data hinder the trained AI models from further fine-tuning for a specific scenario, and ray-tracing data with limited environments lower down the generalization capability of the trained AI models.

Intelligent Communication

Long-tail Cross Modal Hashing

no code implementations28 Nov 2022 Zijun Gao, Jun Wang, Guoxian Yu, Zhongmin Yan, Carlotta Domeniconi, Jinglin Zhang

LtCMH firstly adopts auto-encoders to mine the individuality and commonality of different modalities by minimizing the dependency between the individuality of respective modalities and by enhancing the commonality of these modalities.

Reinforcement Causal Structure Learning on Order Graph

no code implementations22 Nov 2022 Dezhi Yang, Guoxian Yu, Jun Wang, Zhengtian Wu, Maozu Guo

In this paper, we propose {Reinforcement Causal Structure Learning on Order Graph} (RCL-OG) that uses order graph instead of MCMC to model different DAG topological orderings and to reduce the problem size.

Causal Discovery Q-Learning

Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning

no code implementations21 Nov 2022 Junjie Sheng, Lu Wang, Fangkai Yang, Bo Qiao, Hang Dong, Xiangfeng Wang, Bo Jin, Jun Wang, Si Qin, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang

To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem.

Multi-agent Reinforcement Learning reinforcement-learning +1

Contextual Transformer for Offline Meta Reinforcement Learning

no code implementations15 Nov 2022 Runji Lin, Ye Li, Xidong Feng, Zhaowei Zhang, Xian Hong Wu Fung, Haifeng Zhang, Jun Wang, Yali Du, Yaodong Yang

Firstly, we propose prompt tuning for offline RL, where a context vector sequence is concatenated with the input to guide the conditional policy generation.

D4RL Meta Reinforcement Learning +4

CAMANet: Class Activation Map Guided Attention Network for Radiology Report Generation

1 code implementation2 Nov 2022 Jun Wang, Abhir Bhalerao, Terry Yin, Simon See, Yulan He

Radiology report generation (RRG) has gained increasing research attention because of its huge potential to mitigate medical resource shortages and aid the process of disease decision making by radiologists.

Decision Making

Number-Adaptive Prototype Learning for 3D Point Cloud Semantic Segmentation

no code implementations18 Oct 2022 Yangheng Zhao, Jun Wang, Xiaolong Li, Yue Hu, Ce Zhang, Yanfeng Wang, Siheng Chen

Instead of learning a single prototype for each class, in this paper, we propose to use an adaptive number of prototypes to dynamically describe the different point patterns within a semantic class.

3D Semantic Segmentation Scene Understanding +1

Optimizing Vision Transformers for Medical Image Segmentation

1 code implementation14 Oct 2022 Qianying Liu, Chaitanya Kaul, Jun Wang, Christos Anagnostopoulos, Roderick Murray-Smith, Fani Deligianni

For medical image semantic segmentation (MISS), Vision Transformers have emerged as strong alternatives to convolutional neural networks thanks to their inherent ability to capture long-range correlations.

Domain Adaptation Image Segmentation +2

Detecting Backdoors in Deep Text Classifiers

no code implementations11 Oct 2022 You Guo, Jun Wang, Trevor Cohn

Deep neural networks are vulnerable to adversarial attacks, such as backdoor attacks in which a malicious adversary compromises a model during training such that specific behaviour can be triggered at test time by attaching a specific word or phrase to an input.

Data Poisoning text-classification +1

DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases

1 code implementation30 Sep 2022 Donghan Yu, Sheng Zhang, Patrick Ng, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Yiqun Hu, William Wang, Zhiguo Wang, Bing Xiang

Question answering over knowledge bases (KBs) aims to answer natural language questions with factual information such as entities and relations in KBs.

Entity Linking Question Answering +2

Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding

no code implementations28 Sep 2022 Jun Wang, Patrick Ng, Alexander Hanbo Li, Jiarong Jiang, Zhiguo Wang, Ramesh Nallapati, Bing Xiang, Sudipta Sengupta

When synthesizing a SQL query, there is no explicit semantic information of NLQ available to the parser which leads to undesirable generalization performance.

NER Semantic Parsing +1

A Spatial-channel-temporal-fused Attention for Spiking Neural Networks

no code implementations22 Sep 2022 Wuque Cai, Hongze Sun, Rui Liu, Yan Cui, Jun Wang, Yang Xia, Dezhong Yao, Daqing Guo

Spiking neural networks (SNNs) mimic brain computational strategies, and exhibit substantial capabilities in spatiotemporal information processing.

Scale Attention for Learning Deep Face Representation: A Study Against Visual Scale Variation

no code implementations19 Sep 2022 Hailin Shi, Hang Du, Yibo Hu, Jun Wang, Dan Zeng, Ting Yao

Such multi-shot scheme brings inference burden, and the predefined scales inevitably have gap from real data.

Face Recognition

FR: Folded Rationalization with a Unified Encoder

1 code implementation17 Sep 2022 Wei Liu, Haozhao Wang, Jun Wang, Ruixuan Li, Chao Yue, Yuankai Zhang

Conventional works generally employ a two-phase model in which a generator selects the most important pieces, followed by a predictor that makes predictions based on the selected pieces.

ESSumm: Extractive Speech Summarization from Untranscribed Meeting

no code implementations14 Sep 2022 Jun Wang

Extensive results on two well-known meeting datasets (AMI and ICSI corpora) show the effectiveness of our direct speech-based method to improve the summarization quality with untranscribed data.

speech-recognition Speech Recognition

Parameter Estimation of Mixed Gaussian-Impulsive Noise: An U-net++ Based Method

no code implementations6 Sep 2022 Tianfu Qi, Jun Wang, Xiaonan Chen, Wei Huang

In many scenarios, the communication system suffers from both Gaussian white noise and non-Gaussian impulsive noise.

blind source separation

SPCNet: Stepwise Point Cloud Completion Network

4 code implementations5 Sep 2022 Fei Hu, Honghua Chen, Xuequan Lu, Zhe Zhu, Jun Wang, Weiming Wang, Fu Lee Wang, Mingqiang Wei

We propose a novel stepwise point cloud completion network (SPCNet) for various 3D models with large missings.

Point Cloud Completion

Geometric and Learning-based Mesh Denoising: A Comprehensive Survey

no code implementations2 Sep 2022 Honghua Chen, Mingqiang Wei, Jun Wang

In this work, we provide a comprehensive review of the advances in mesh denoising, containing both traditional geometric approaches and recent learning-based methods.

Denoising

Which country is this picture from? New data and methods for DNN-based country recognition

1 code implementation2 Sep 2022 Omran Alamayreh, Giovanna Maria Dimitri, Jun Wang, Benedetta Tondi, Mauro Barni

Notably, we found that asking the network to identify the country provides better results than estimating the geo-coordinates and then tracing them back to the country where the picture was taken.

MODNet: Multi-offset Point Cloud Denoising Network Customized for Multi-scale Patches

1 code implementation30 Aug 2022 Anyi Huang, Qian Xie, Zhoutao Wang, Dening Lu, Mingqiang Wei, Jun Wang

Second, a multi-scale perception module is designed to embed multi-scale geometric information for each scale feature and regress multi-scale weights to guide a multi-offset denoising displacement.

Denoising

Joint Optimization of Resource Allocation, Phase Shift and UAV Trajectory for Energy-Efficient RIS-Assisted UAV-Enabled MEC Systems

no code implementations30 Aug 2022 Xintong Qin, Zhengyu Song, Tianwei Hou, Wenjuan Yu, Jun Wang, Xin Sun

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) has been deemed a promising paradigm to provide ubiquitous communication and computing services for the Internet of Things (IoT).

Edge-computing

Dual Representation Learning for One-Step Clustering of Multi-View Data

1 code implementation30 Aug 2022 Wei zhang, Zhaohong Deng, Kup-Sze Choi, Jun Wang, Shitong Wang

Meanwhile, to make the representation learning more specific to the clustering task, a one-step learning framework is proposed to integrate representation learning and clustering partition as a whole.

Clustering Representation Learning

A Comprehensive Survey on Aerial Mobile Edge Computing: Challenges, State-of-the-Art, and Future Directions

no code implementations30 Aug 2022 Zhengyu Song, Xintong Qin, Yuanyuan Hao, Tianwei Hou, Jun Wang, Xin Sun

Driven by the visions of Internet of Things (IoT), there is an ever-increasing demand for computation resources of IoT users to support diverse applications.

Edge-computing Scheduling

Lane Change Classification and Prediction with Action Recognition Networks

1 code implementation24 Aug 2022 Kai Liang, Jun Wang, Abhir Bhalerao

Previous works often adopt physical variables such as driving speed, acceleration and so forth for lane change classification.

Action Recognition Autonomous Driving +2

AA-Forecast: Anomaly-Aware Forecast for Extreme Events

1 code implementation21 Aug 2022 Ashkan Farhangi, Jiang Bian, Arthur Huang, Haoyi Xiong, Jun Wang, Zhishan Guo

Moreover, the framework employs a dynamic uncertainty optimization algorithm that reduces the uncertainty of forecasts in an online manner.

Anomaly Forecasting Management +3

Multi-View Pre-Trained Model for Code Vulnerability Identification

no code implementations10 Aug 2022 Xuxiang Jiang, Yinhao Xiao, Jun Wang, Wei zhang

Vulnerability identification is crucial for cyber security in the software-related industry.

Contrastive Learning

A high-resolution dynamical view on momentum methods for over-parameterized neural networks

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

Due to the simplicity and efficiency of the first-order gradient method, it has been widely used in training neural networks.

UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration

1 code implementation4 Aug 2022 Zhilei Chen, Honghua Chen, Lina Gong, Xuefeng Yan, Jun Wang, Yanwen Guo, Jing Qin, Mingqiang Wei

High-confidence overlap prediction and accurate correspondences are critical for cutting-edge models to align paired point clouds in a partial-to-partial manner.

Point Cloud Registration

TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation

1 code implementation3 Aug 2022 Jun Wang, Mingfei Gao, Yuqian Hu, Ramprasaath R. Selvaraju, Chetan Ramaiah, ran Xu, Joseph F. JaJa, Larry S. Davis

To address this deficiency, we develop a new method to generate high-quality and diverse QA pairs by explicitly utilizing the existing rich text available in the scene context of each image.

Answer Generation Question-Answer-Generation +3

Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL

no code implementations2 Aug 2022 Jakub Grudzien Kuba, Xidong Feng, Shiyao Ding, Hao Dong, Jun Wang, Yaodong Yang

The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in the artificial intelligence (AI) research community.

Multi-agent Reinforcement Learning

CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completion

no code implementations1 Aug 2022 Zhe Zhu, Liangliang Nan, Haoran Xie, Honghua Chen, Mingqiang Wei, Jun Wang, Jing Qin

The first module transfers the intrinsic shape characteristics from single images to guide the geometry generation of the missing regions of point clouds, in which we propose IPAdaIN to embed the global features of both the image and the partial point cloud into completion.

Point Cloud Completion

Scalable Model-based Policy Optimization for Decentralized Networked Systems

2 code implementations13 Jul 2022 Yali Du, Chengdong Ma, Yuchen Liu, Runji Lin, Hao Dong, Jun Wang, Yaodong Yang

Reinforcement learning algorithms require a large amount of samples; this often limits their real-world applications on even simple tasks.

Cross-modal Prototype Driven Network for Radiology Report Generation

1 code implementation11 Jul 2022 Jun Wang, Abhir Bhalerao, Yulan He

Radiology report generation (RRG) aims to describe automatically a radiology image with human-like language and could potentially support the work of radiologists, reducing the burden of manual reporting.

A simple normalization technique using window statistics to improve the out-of-distribution generalization on medical images

1 code implementation7 Jul 2022 Chengfeng Zhou, Songchang Chen, Chenming Xu, Jun Wang, Feng Liu, Chun Zhang, Juan Ye, Hefeng Huang, Dahong Qian

In this study, we present a novel normalization technique called window normalization (WIN) to improve the model generalization on heterogeneous medical images, which is a simple yet effective alternative to existing normalization methods.

Breast Cancer Detection Out-of-Distribution Generalization

ImLoveNet: Misaligned Image-supported Registration Network for Low-overlap Point Cloud Pairs

no code implementations2 Jul 2022 Honghua Chen, Zeyong Wei, Yabin Xu, Mingqiang Wei, Jun Wang

Low-overlap regions between paired point clouds make the captured features very low-confidence, leading cutting edge models to point cloud registration with poor quality.

Point Cloud Registration

Effects of Safety State Augmentation on Safe Exploration

1 code implementation6 Jun 2022 Aivar Sootla, Alexander I. Cowen-Rivers, Jun Wang, Haitham Bou Ammar

We further show that Simmer can stabilize training and improve the performance of safe RL with average constraints.

Reinforcement Learning (RL) Safe Exploration +1

Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints

no code implementations31 May 2022 David Mguni, Aivar Sootla, Juliusz Ziomek, Oliver Slumbers, Zipeng Dai, Kun Shao, Jun Wang

In this paper, we introduce a reinforcement learning (RL) framework named \textbf{L}earnable \textbf{I}mpulse \textbf{C}ontrol \textbf{R}einforcement \textbf{A}lgorithm (LICRA), for learning to optimally select both when to act and which actions to take when actions incur costs.

Reinforcement Learning (RL)

Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images

no code implementations31 May 2022 Jun Shi, Yuanming Zhang, Zheng Li, Xiangmin Han, Saisai Ding, Jun Wang, Shihui Ying

In this work, we propose a pseudo-data based self-supervised federated learning (FL) framework, named SSL-FT-BT, to improve both the diagnostic accuracy and generalization of CAD models.

Contrastive Learning Federated Learning +1

Multi-Agent Reinforcement Learning is a Sequence Modeling Problem

1 code implementation30 May 2022 Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, Yaodong Yang

In this paper, we introduce a novel architecture named Multi-Agent Transformer (MAT) that effectively casts cooperative multi-agent reinforcement learning (MARL) into SM problems wherein the task is to map agents' observation sequence to agents' optimal action sequence.

Decision Making Multi-agent Reinforcement Learning +2

A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems

no code implementations30 May 2022 Oliver Slumbers, David Henry Mguni, Stephen Marcus McAleer, Stefano B. Blumberg, Jun Wang, Yaodong Yang

Although there are equilibrium concepts in game theory that take into account risk aversion, they either assume that agents are risk-neutral with respect to the uncertainty caused by the actions of other agents, or they are not guaranteed to exist.

Autonomous Driving Multi-agent Reinforcement Learning

SEREN: Knowing When to Explore and When to Exploit

no code implementations30 May 2022 Changmin Yu, David Mguni, Dong Li, Aivar Sootla, Jun Wang, Neil Burgess

Efficient reinforcement learning (RL) involves a trade-off between "exploitative" actions that maximise expected reward and "explorative'" ones that sample unvisited states.

Reinforcement Learning (RL)

Sample-Efficient Optimisation with Probabilistic Transformer Surrogates

no code implementations27 May 2022 Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Rasul Tutunov, Jun Wang, Haitham Bou Ammar

First, we notice that these models are trained on uniformly distributed inputs, which impairs predictive accuracy on non-uniform data - a setting arising from any typical BO loop due to exploration-exploitation trade-offs.

Bayesian Optimisation Gaussian Processes

Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission

no code implementations26 May 2022 Jun Wang, Sixian Wang, Jincheng Dai, Zhongwei Si, Dekun Zhou, Kai Niu

However, current deep JSCC image transmission systems are typically optimized for traditional distortion metrics such as peak signal-to-noise ratio (PSNR) or multi-scale structural similarity (MS-SSIM).

MS-SSIM SSIM +1

Multi-Agent Feedback Enabled Neural Networks for Intelligent Communications

1 code implementation22 May 2022 Fanglei Sun, Yang Li, Ying Wen, Jingchen Hu, Jun Wang, Yang Yang, Kai Li

The design of MAFENN framework and algorithm are dedicated to enhance the learning capability of the feedfoward DL networks or their variations with the simple data feedback.

Denoising Intelligent Communication

A Review of Safe Reinforcement Learning: Methods, Theory and Applications

1 code implementation20 May 2022 Shangding Gu, Long Yang, Yali Du, Guang Chen, Florian Walter, Jun Wang, Yaodong Yang, Alois Knoll

To establish a good foundation for future research in this thread, in this paper, we provide a review for safe RL from the perspectives of methods, theory and applications.

Autonomous Driving Decision Making +3

An Architecture for the detection of GAN-generated Flood Images with Localization Capabilities

no code implementations14 May 2022 Jun Wang, Omran Alamayreh, Benedetta Tondi, Mauro Barni

In this paper, we address a new image forensics task, namely the detection of fake flood images generated by ClimateGAN architecture.

Image Forensics

Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech

1 code implementation ACL 2022 Yang Li, Cheng Yu, Guangzhi Sun, Hua Jiang, Fanglei Sun, Weiqin Zu, Ying Wen, Yang Yang, Jun Wang

Modelling prosody variation is critical for synthesizing natural and expressive speech in end-to-end text-to-speech (TTS) systems.

On the Convergence of Fictitious Play: A Decomposition Approach

no code implementations3 May 2022 Yurong Chen, Xiaotie Deng, Chenchen Li, David Mguni, Jun Wang, Xiang Yan, Yaodong Yang

Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in $n$-player games, which builds the foundation for modern multi-agent learning algorithms.

BI-GreenNet: Learning Green's functions by boundary integral network

no code implementations28 Apr 2022 Guochang Lin, Fukai Chen, Pipi Hu, Xiang Chen, Junqing Chen, Jun Wang, Zuoqiang Shi

In addition, we also use the Green's function calculated by our method to solve a class of PDE, and also obtain high-precision solutions, which shows the good generalization ability of our method on solving PDEs.

M2N: Mesh Movement Networks for PDE Solvers

1 code implementation24 Apr 2022 Wenbin Song, Mingrui Zhang, Joseph G. Wallwork, Junpeng Gao, Zheng Tian, Fanglei Sun, Matthew D. Piggott, Junqing Chen, Zuoqiang Shi, Xiang Chen, Jun Wang

However, mesh movement methods, such as the Monge-Ampere method, require the solution of auxiliary equations, which can be extremely expensive especially when the mesh is adapted frequently.

Graph Attention

FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis

2 code implementations21 Apr 2022 Rongjie Huang, Max W. Y. Lam, Jun Wang, Dan Su, Dong Yu, Yi Ren, Zhou Zhao

Also, FastDiff enables a sampling speed of 58x faster than real-time on a V100 GPU, making diffusion models practically applicable to speech synthesis deployment for the first time.

Ranked #7 on Text-To-Speech Synthesis on LJSpeech (using extra training data)

Denoising Speech Synthesis +2

Deep Algebraic Fitting for Multiple Circle Primitives Extraction from Raw Point Clouds

no code implementations2 Apr 2022 Zeyong Wei, Honghua Chen, Hao Tang, Qian Xie, Mingqiang Wei, Jun Wang

The shape of circle is one of fundamental geometric primitives of man-made engineering objects.

BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis

1 code implementation ICLR 2022 Max W. Y. Lam, Jun Wang, Dan Su, Dong Yu

We propose a new bilateral denoising diffusion model (BDDM) that parameterizes both the forward and reverse processes with a schedule network and a score network, which can train with a novel bilateral modeling objective.

Image Generation Speech Synthesis

Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds

1 code implementation23 Mar 2022 Haoran Zhou, Honghua Chen, Yingkui Zhang, Mingqiang Wei, Haoran Xie, Jun Wang, Tong Lu, Jing Qin, Xiao-Ping Zhang

Differently, our network is designed to refine the initial normal of each point by extracting additional information from multiple feature representations.

PointAttN: You Only Need Attention for Point Cloud Completion

1 code implementation16 Mar 2022 Jun Wang, Ying Cui, Dongyan Guo, Junxia Li, Qingshan Liu, Chunhua Shen

To solve the problems, we leverage the cross-attention and self-attention mechanisms to design novel neural network for processing point cloud in a per-point manner to eliminate kNNs.

Point Cloud Completion

Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization

2 code implementations4 Mar 2022 Minghuan Liu, Zhengbang Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao, Yong Yu, Jun Wang

Recent progress in state-only imitation learning extends the scope of applicability of imitation learning to real-world settings by relieving the need for observing expert actions.

Imitation Learning Transfer Learning

CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer

no code implementations2 Mar 2022 Xianbin Ye, Ziliang Li, Fei Ma, Zongbi Yi, Pengyong Li, Jun Wang, Peng Gao, Yixuan Qiao, Guotong Xie

Anti-cancer drug discoveries have been serendipitous, we sought to present the Open Molecular Graph Learning Benchmark, named CandidateDrug4Cancer, a challenging and realistic benchmark dataset to facilitate scalable, robust, and reproducible graph machine learning research for anti-cancer drug discovery.

Drug Discovery Graph Learning

Understanding Adversarial Robustness from Feature Maps of Convolutional Layers

1 code implementation25 Feb 2022 Cong Xu, Wei zhang, Jun Wang, Min Yang

Our theoretical analysis discovers that larger convolutional feature maps before average pooling can contribute to better resistance to perturbations, but the conclusion is not true for max pooling.

Adversarial Robustness

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