Search Results for author: Yu Wang

Found 282 papers, 106 papers with code

Pseudo-Masked Language Models for Unified Language Model Pre-Training

1 code implementation ICML 2020 Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Xiaodong Liu, Yu Wang, Jianfeng Gao, Songhao Piao, Ming Zhou, Hsiao-Wuen Hon

We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM).

Language Modelling Natural Language Understanding

双重否定结构自动识别研究(The Research on Automatic Recognition of the Double Negation Structure)

no code implementations CCL 2022 Yu Wang, Yulin Yuan

“双重否定结构是一种“通过两次否定表示肯定意义”的特殊结构, 其存在会对自然语言处理中的语义判断与情感分类产生重要影响。本文以“eg eg P== extgreater P”为标准, 对现代汉语中所有的“否定词+否定词”结构进行了遍历研究, 将双重否定结构按照格式分为了3大类, 25小类, 常用双重否定结构或构式132个。结合动词的叙实性、否定焦点、语义否定与语用否定等相关理论, 本文归纳了双重否定结构的三大成立条件, 并据此设计实现了基于规则的双重否定结构自动识别程序。程序实验的精确率为98. 85%, 召回率为98. 90%, F1值为98. 85%。同时, 程序还从96281句语料中获得了8640句精确率约为99%的含有双重否定结构的句子, 为后续基于统计的深度学习模型提供了语料支持的可能。”


no code implementations ICLR 2019 Yu Wang, Jack W. Stokes, Mady Marinescu

Antimalware products are a key component in detecting malware attacks, and their engines typically execute unknown programs in a sandbox prior to running them on the native operating system.

reinforcement-learning Reinforcement Learning (RL)

CNNSAT: Fast, Accurate Boolean Satisfiability using Convolutional Neural Networks

no code implementations ICLR 2019 Yu Wang, Fengjuan Gao, Amin Alipour, Linzhang Wang, Xuandong Li, Zhendong Su

Boolean satisfiability (SAT) is one of the most well-known NP-complete problems and has been extensively studied.

HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction

no code implementations EMNLP 2020 Yu Wang, Yun Li, Hanghang Tong, Ziye Zhu

Specifically, we design (1) Head-Tail Detector based on the multi-head self-attention mechanism and bi-affine classifier to detect boundary tokens, and (2) Token Interaction Tagger based on traditional sequence labeling approaches to characterize the internal token connection within the boundary.

named-entity-recognition Named Entity Recognition +2

基于规则的双重否定识别——以“不v1不v2”为例(Double Negative Recognition Based on Rules——Taking “不v1不v2” as an Example)

no code implementations CCL 2020 Yu Wang

“不v1不v2”是汉语中典型的双重否定结构形式之一, 它包括“不+助动词+不+v2”(不得不去)、“不+是+不v2”(不是不好)、述宾结构“不v1... 不v2”(不认为他不去)等多种双重否定结构, 情况复杂。本文以“不v1不v2”为例, 结合“元语否定”、“动词叙实性”、“否定焦点”等概念, 对“不v1不v2”进行了全面的考察, 制定了“不v1不v2”双重否定结构的识别策略。根据识别策略, 设计了双重否定自动识别程序, 并在此过程中补充了助动词表、非叙实动词表等词库。最终, 对28033句语料进行了识别, 识别正确率为97. 87%, 召回率约为93. 10%。

OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Control

no code implementations22 Sep 2023 Botian Xu, Feng Gao, Chao Yu, Ruize Zhang, Yi Wu, Yu Wang

In this work, we introduce OmniDrones, an efficient and flexible platform tailored for reinforcement learning in drone control, built on Nvidia's Omniverse Isaac Sim.

A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications

1 code implementation31 Aug 2023 Yi Zhang, Yuying Zhao, Zhaoqing Li, Xueqi Cheng, Yu Wang, Olivera Kotevska, Philip S. Yu, Tyler Derr

Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain.

Privacy Preserving

Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition

no code implementations28 Aug 2023 Zhisheng Zheng, Ziyang Ma, Yu Wang, Xie Chen

In recent years, speech-based self-supervised learning (SSL) has made significant progress in various tasks, including automatic speech recognition (ASR).

Active Learning Automatic Speech Recognition +3

Label Denoising through Cross-Model Agreement

no code implementations27 Aug 2023 Yu Wang, Xin Xin, Zaiqiao Meng, Xiangnan He, Joemon Jose, Fuli Feng

We employ the proposed DeCA on both the binary label scenario and the multiple label scenario.

Denoising Image Classification

Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory

no code implementations24 Aug 2023 Karthik Somayaji NS, Yu Wang, Malachi Schram, Jan Drgona, Mahantesh Halappanavar, Frank Liu, Peng Li

Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value function distribution.

reinforcement-learning Reinforcement Learning (RL)

Knowledge Graph Prompting for Multi-Document Question Answering

no code implementations22 Aug 2023 Yu Wang, Nedim Lipka, Ryan A. Rossi, Alexa Siu, Ruiyi Zhang, Tyler Derr

Concurrently, the LM-guided traverser acts as a local navigator that gathers pertinent context to progressively approach the question and guarantee retrieval quality.

graph construction Open-Domain Question Answering +1

LibriSQA: Advancing Free-form and Open-ended Spoken Question Answering with a Novel Dataset and Framework

1 code implementation20 Aug 2023 Zihan Zhao, Yiyang Jiang, Heyang Liu, Yanfeng Wang, Yu Wang

While Large Language Models (LLMs) have demonstrated commendable performance across a myriad of domains and tasks, existing LLMs still exhibit a palpable deficit in handling multimodal functionalities, especially for the Spoken Question Answering (SQA) task which necessitates precise alignment and deep interaction between speech and text features.

Multiple-choice Question Answering

DeSCo: Towards Generalizable and Scalable Deep Subgraph Counting

no code implementations16 Aug 2023 Tianyu Fu, Chiyue Wei, Yu Wang, Rex Ying

Subgraph counting is the problem of counting the occurrences of a given query graph in a large target graph.

Graph Regression Subgraph Counting

Isomer: Isomerous Transformer for Zero-shot Video Object Segmentation

1 code implementation13 Aug 2023 Yichen Yuan, Yifan Wang, Lijun Wang, Xiaoqi Zhao, Huchuan Lu, Yu Wang, Weibo Su, Lei Zhang

Recent leading zero-shot video object segmentation (ZVOS) works devote to integrating appearance and motion information by elaborately designing feature fusion modules and identically applying them in multiple feature stages.

Semantic Segmentation Video Object Segmentation +2

Learning Concise and Descriptive Attributes for Visual Recognition

no code implementations7 Aug 2023 An Yan, Yu Wang, Yiwu Zhong, chengyu dong, Zexue He, Yujie Lu, William Wang, Jingbo Shang, Julian McAuley

Recent advances in foundation models present new opportunities for interpretable visual recognition -- one can first query Large Language Models (LLMs) to obtain a set of attributes that describe each class, then apply vision-language models to classify images via these attributes.


Model Provenance via Model DNA

no code implementations4 Aug 2023 Xin Mu, Yu Wang, Yehong Zhang, JiaQi Zhang, Hui Wang, Yang Xiang, Yue Yu

Understanding the life cycle of the machine learning (ML) model is an intriguing area of research (e. g., understanding where the model comes from, how it is trained, and how it is used).

Representation Learning

Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding

no code implementations28 Jul 2023 Xuefei Ning, Zinan Lin, Zixuan Zhou, Huazhong Yang, Yu Wang

This work aims at decreasing the end-to-end generation latency of large language models (LLMs).

Audio-aware Query-enhanced Transformer for Audio-Visual Segmentation

no code implementations25 Jul 2023 Jinxiang Liu, Chen Ju, Chaofan Ma, Yanfeng Wang, Yu Wang, Ya zhang

The goal of the audio-visual segmentation (AVS) task is to segment the sounding objects in the video frames using audio cues.

Ada3D : Exploiting the Spatial Redundancy with Adaptive Inference for Efficient 3D Object Detection

no code implementations17 Jul 2023 Tianchen Zhao, Xuefei Ning, Ke Hong, Zhongyuan Qiu, Pu Lu, Yali Zhao, Linfeng Zhang, Lipu Zhou, Guohao Dai, Huazhong Yang, Yu Wang

One reason for this high resource consumption is the presence of a large number of redundant background points in Lidar point clouds, resulting in spatial redundancy in both 3D voxel and dense BEV map representations.

3D Object Detection Autonomous Driving +1

A Novel Multi-Task Model Imitating Dermatologists for Accurate Differential Diagnosis of Skin Diseases in Clinical Images

no code implementations17 Jul 2023 Yan-Jie Zhou, Wei Liu, Yuan Gao, Jing Xu, Le Lu, Yuping Duan, Hao Cheng, Na Jin, Xiaoyong Man, Shuang Zhao, Yu Wang

Skin diseases are among the most prevalent health issues, and accurate computer-aided diagnosis methods are of importance for both dermatologists and patients.

Multi-Task Learning

LINFA: a Python library for variational inference with normalizing flow and annealing

1 code implementation10 Jul 2023 Yu Wang, Emma R. Cobian, Jubilee Lee, Fang Liu, Jonathan D. Hauenstein, Daniele E. Schiavazzi

Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions.

Variational Inference

Fairness and Diversity in Recommender Systems: A Survey

no code implementations10 Jul 2023 Yuying Zhao, Yu Wang, Yunchao Liu, Xueqi Cheng, Charu Aggarwal, Tyler Derr

Additionally, motivated by the concepts of user-level and item-level fairness, we broaden the understanding of diversity to encompass not only the item level but also the user level.

Fairness Recommendation Systems

Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023

no code implementations20 Jun 2023 Yu Wang, Tiebiao Zhao, Fan Yi

This technical report presents our 1st place solution for the Waymo Open Sim Agents Challenge (WOSAC) 2023.

motion prediction

Pushing the Limits of 3D Shape Generation at Scale

no code implementations20 Jun 2023 Yu Wang, Xuelin Qian, Jingyang Huo, Tiejun Huang, Bo Zhao, Yanwei Fu

Through the adaptation of the Auto-Regressive model and the utilization of large language models, we have developed a remarkable model with an astounding 3. 6 billion trainable parameters, establishing it as the largest 3D shape generation model to date, named Argus-3D.

3D Shape Generation Quantization +1

Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation

no code implementations15 Jun 2023 Yu Wang, Tongya Zheng, Shunyu Liu, KaiXuan Chen, Zunlei Feng, Yunzhi Hao, Mingli Song

The human mobility simulation task aims to generate human mobility trajectories given a small set of trajectory data, which have aroused much concern due to the scarcity and sparsity of human mobility data.

Pushing the Limits of Unsupervised Unit Discovery for SSL Speech Representation

no code implementations15 Jun 2023 Ziyang Ma, Zhisheng Zheng, Guanrou Yang, Yu Wang, Chao Zhang, Xie Chen

Our models outperform other SSL models significantly on the LibriSpeech benchmark without the need for iterative re-clustering and re-training.

Clustering Language Modelling +2

OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models

1 code implementation15 Jun 2023 Enshu Liu, Xuefei Ning, Zinan Lin, Huazhong Yang, Yu Wang

Diffusion probabilistic models (DPMs) are a new class of generative models that have achieved state-of-the-art generation quality in various domains.

Enhanced Multimodal Representation Learning with Cross-modal KD

no code implementations CVPR 2023 Mengxi Chen, Linyu Xing, Yu Wang, Ya zhang

This paper explores the tasks of leveraging auxiliary modalities which are only available at training to enhance multimodal representation learning through cross-modal Knowledge Distillation (KD).

Contrastive Learning Emotion Classification +5

Design Principles for Generalization and Scalability of AI in Communication Systems

no code implementations9 Jun 2023 Pablo Soldati, Euhanna Ghadimi, Burak Demirel, Yu Wang, Raimundas Gaigalas, Mathias Sintorn

Artificial intelligence (AI) has emerged as a powerful tool for addressing complex and dynamic tasks in communication systems, where traditional rule-based algorithms often struggle.


SelfEvolve: A Code Evolution Framework via Large Language Models

no code implementations5 Jun 2023 Shuyang Jiang, Yuhao Wang, Yu Wang

However, while various methods have been proposed to augment LLMs with retrieved knowledge and enhance the quality of code generation, the performance of these retrieval-based methods is limited by the strength of the retrievers used.

Code Generation Retrieval

CDLT: A Dataset with Concept Drift and Long-Tailed Distribution for Fine-Grained Visual Categorization

no code implementations4 Jun 2023 Shuo Ye, Yufeng Shi, Ruxin Wang, Yu Wang, Jiamiao Xu, Chuanwu Yang, Xinge You

Data is the foundation for the development of computer vision, and the establishment of datasets plays an important role in advancing the techniques of fine-grained visual categorization~(FGVC).

Fine-Grained Visual Categorization

Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning

1 code implementation CVPR 2023 Yu Wang, Pengchong Qiao, Chang Liu, Guoli Song, Xiawu Zheng, Jie Chen

We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field.

Annotation-free Audio-Visual Segmentation

no code implementations18 May 2023 Jinxiang Liu, Yu Wang, Chen Ju, Chaofan Ma, Ya zhang, Weidi Xie

The objective of Audio-Visual Segmentation (AVS) is to localise the sounding objects within visual scenes by accurately predicting pixel-wise segmentation masks.

Image Segmentation Semantic Segmentation

Conditional Denoising Diffusion for Sequential Recommendation

no code implementations22 Apr 2023 Yu Wang, Zhiwei Liu, Liangwei Yang, Philip S. Yu

Generative models have attracted significant interest due to their ability to handle uncertainty by learning the inherent data distributions.

Denoising Sequential Recommendation

HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion

no code implementations10 Apr 2023 Yu Wang, Shuhui Bu, Lin Chen, Yifei Dong, Kun Li, Xuefeng Cao, Ke Li

First, the point cloud is divided into small patches, and a matching patch set is selected based on global descriptors and spatial distribution, which constitutes the coarse matching process.

Point Cloud Registration

Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation

1 code implementation CVPR 2023 Tianli Zhang, Mengqi Xue, Jiangtao Zhang, Haofei Zhang, Yu Wang, Lechao Cheng, Jie Song, Mingli Song

Most existing online knowledge distillation(OKD) techniques typically require sophisticated modules to produce diverse knowledge for improving students' generalization ability.

Knowledge Distillation

LiDARFormer: A Unified Transformer-based Multi-task Network for LiDAR Perception

no code implementations21 Mar 2023 Zixiang Zhou, Dongqiangzi Ye, Weijia Chen, Yufei Xie, Yu Wang, Panqu Wang, Hassan Foroosh

The proposed LiDARFormer utilizes cross-space global contextual feature information and exploits cross-task synergy to boost the performance of LiDAR perception tasks across multiple large-scale datasets and benchmarks.

Multi-Task Learning Semantic Segmentation

DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery

no code implementations17 Mar 2023 Chaofan Ma, Yuhuan Yang, Chen Ju, Fei Zhang, Jinxiang Liu, Yu Wang, Ya zhang, Yanfeng Wang

However, the challenges exist as there is one structural difference between generative and discriminative models, which limits the direct use.

Object Discovery Object Localization

Interpretable Outlier Summarization

no code implementations11 Mar 2023 Yu Wang, Lei Cao, Yizhou Yan, Samuel Madden

Moreover, to effectively handle high dimensional, highly complex data sets which are hard to summarize with simple rules, we propose a localized STAIR approach, called L-STAIR.

Anomaly Detection Outlier Detection

Demystifying What Code Summarization Models Learned

no code implementations4 Mar 2023 Yu Wang, Ke Wang

Based on these findings, we present two example uses of the formal definition of patterns: a new method for evaluating the robustness and a new technique for improving the accuracy of code summarization models.

Code Summarization Image Classification

A Targeted Accuracy Diagnostic for Variational Approximations

1 code implementation24 Feb 2023 Yu Wang, Mikołaj Kasprzak, Jonathan H. Huggins

Variational Inference (VI) is an attractive alternative to Markov Chain Monte Carlo (MCMC) due to its computational efficiency in the case of large datasets and/or complex models with high-dimensional parameters.

Variational Inference

Knowledge-aware Bayesian Co-attention for Multimodal Emotion Recognition

no code implementations20 Feb 2023 Zihan Zhao, Yu Wang, Yanfeng Wang

Multimodal emotion recognition is a challenging research area that aims to fuse different modalities to predict human emotion.

Multimodal Emotion Recognition

Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Records

1 code implementation11 Feb 2023 Bingyue Su, Yu Wang, Daniele E. Schiavazzi, Fang Liu

We use normalizing flows (NF), a family of deep generative models, to estimate the probability density of a dataset with differential privacy (DP) guarantees, from which privacy-preserving synthetic data are generated.

Density Estimation Privacy Preserving +1

Toward Extremely Lightweight Distracted Driver Recognition With Distillation-Based Neural Architecture Search and Knowledge Transfer

1 code implementation9 Feb 2023 Dichao Liu, Toshihiko Yamasaki, Yu Wang, Kenji Mase, Jien Kato

Experimental results on the Statefarm Distracted Driver Detection Dataset and AUC Distracted Driver Dataset show that the proposed approach is highly effective for recognizing distracted driving behaviors from photos: (1) the teacher network's accuracy surpasses the previous best accuracy; (2) the student network achieves very high accuracy with only 0. 42M parameters (around 55% of the previous most lightweight model).

Knowledge Distillation Neural Architecture Search +1

Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation

no code implementations8 Feb 2023 Xinyi Yang, Shiyu Huang, Yiwen Sun, Yuxiang Yang, Chao Yu, Wei-Wei Tu, Huazhong Yang, Yu Wang

Goal-conditioned hierarchical reinforcement learning (HRL) provides a promising direction to tackle this challenge by introducing a hierarchical structure to decompose the search space, where the low-level policy predicts primitive actions in the guidance of the goals derived from the high-level policy.

Hierarchical Reinforcement Learning Multi-agent Reinforcement Learning +2

Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"

1 code implementation2 Feb 2023 Junbo Zhao, Xuefei Ning, Enshu Liu, Binxin Ru, Zixuan Zhou, Tianchen Zhao, Chen Chen, Jiajin Zhang, Qingmin Liao, Yu Wang

In the first step, we train different sub-predictors on different types of available low-fidelity information to extract beneficial knowledge as low-fidelity experts.

Neural Architecture Search

Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes

no code implementations15 Jan 2023 Yu Wang

Fourth, it proposes a modular and interpretable framework for unsupervised and weakly-supervised probabilistic topic modeling of time-varying data that combines generative statistical models with computational geometric methods.

EEG Electroencephalogram (EEG) +2

Two-Stream Networks for Weakly-Supervised Temporal Action Localization With Semantic-Aware Mechanisms

no code implementations CVPR 2023 Yu Wang, Yadong Li, Hongbin Wang

In this paper, we hypothesize that snippets with similar representations should be considered as the same action class despite the absence of supervision signals on each snippet.

Multiple Instance Learning Weakly-supervised Temporal Action Localization +1

DDH-QA: A Dynamic Digital Humans Quality Assessment Database

1 code implementation24 Dec 2022 ZiCheng Zhang, Yingjie Zhou, Wei Sun, Wei Lu, Xiongkuo Min, Yu Wang, Guangtao Zhai

In recent years, large amounts of effort have been put into pushing forward the real-world application of dynamic digital human (DDH).

Video Quality Assessment

Detecting Objects with Graph Priors and Graph Refinement

no code implementations23 Dec 2022 Aritra Bhowmik, Martin R. Oswald, Yu Wang, Nora Baka, Cees G. M. Snoek

The key idea of our paper is to model object relations as a function of initial class predictions and co-occurrence priors to generate a graph representation of an image for improved classification and bounding box regression.

Hybrid Rule-Neural Coreference Resolution System based on Actor-Critic Learning

no code implementations20 Dec 2022 Yu Wang, Hongxia Jin

A coreference resolution system is to cluster all mentions that refer to the same entity in a given context.


A Robust Semantic Frame Parsing Pipeline on a New Complex Twitter Dataset

no code implementations18 Dec 2022 Yu Wang, Hongxia Jin

In this paper, we introduce a robust semantic frame parsing pipeline that can handle both \emph{OOD} patterns and \emph{OOV} tokens in conjunction with a new complex Twitter dataset that contains long tweets with more \emph{OOD} patterns and \emph{OOV} tokens.

Semantic Frame Parsing Spoken Language Understanding

Neural Coreference Resolution based on Reinforcement Learning

no code implementations18 Dec 2022 Yu Wang, Hongxia Jin

The target of a coreference resolution system is to cluster all mentions that refer to the same entity in a given context.

Clustering coreference-resolution +2

Localized Contrastive Learning on Graphs

no code implementations8 Dec 2022 Hengrui Zhang, Qitian Wu, Yu Wang, Shaofeng Zhang, Junchi Yan, Philip S. Yu

Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data.

Contrastive Learning Data Augmentation +1

Fairness and Explainability: Bridging the Gap Towards Fair Model Explanations

1 code implementation7 Dec 2022 Yuying Zhao, Yu Wang, Tyler Derr

Although research efforts have been devoted to measuring and mitigating bias, they mainly study bias from the result-oriented perspective while neglecting the bias encoded in the decision-making procedure.

Decision Making Fairness

Multi-view deep learning based molecule design and structural optimization accelerates the SARS-CoV-2 inhibitor discovery

no code implementations3 Dec 2022 Chao Pang, Yu Wang, Yi Jiang, Ruheng Wang, Ran Su, Leyi Wei

Moreover, case study results on targeted molecule generation for the SARS-CoV-2 main protease (Mpro) show that by integrating molecule docking into our model as chemical priori, we successfully generate new small molecules with desired drug-like properties for the Mpro, potentially accelerating the de novo design of Covid-19 drugs.

Benchmarking Representation Learning

Few-Shot Specific Emitter Identification via Hybrid Data Augmentation and Deep Metric Learning

no code implementations1 Dec 2022 Cheng Wang, Xue Fu, Yu Wang, Guan Gui, Haris Gacanin, Hikmet Sari, Fumiyuki Adachi

Specific emitter identification (SEI) is a potential physical layer authentication technology, which is one of the most critical complements of upper layer authentication.

Data Augmentation Metric Learning

A Bayesian Approach to Reconstructing Interdependent Infrastructure Networks from Cascading Failures

no code implementations28 Nov 2022 Yu Wang, Jin-Zhu Yu, Hiba Baroud

We propose a scalable nonparametric Bayesian approach to reconstruct the topology of interdependent infrastructure networks from observations of cascading failures.

SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement

1 code implementation15 Nov 2022 Zhaofan Qiu, Yehao Li, Yu Wang, Yingwei Pan, Ting Yao, Tao Mei

In this paper, we propose a novel deep architecture tailored for 3D point cloud applications, named as SPE-Net.

Cross-layer Attention Network for Fine-grained Visual Categorization

no code implementations17 Oct 2022 Ranran Huang, Yu Wang, Huazhong Yang

Learning discriminative representations for subtle localized details plays a significant role in Fine-grained Visual Categorization (FGVC).

Fine-Grained Visual Categorization

Controlling Bias Exposure for Fair Interpretable Predictions

no code implementations14 Oct 2022 Zexue He, Yu Wang, Julian McAuley, Bodhisattwa Prasad Majumder

However, when sensitive information is semantically entangled with the task information of the input, e. g., gender information is predictive for a profession, a fair trade-off between task performance and bias mitigation is difficult to achieve.

text-classification Text Classification

Out-of-Distribution Detection with Hilbert-Schmidt Independence Optimization

1 code implementation26 Sep 2022 Jingyang Lin, Yu Wang, Qi Cai, Yingwei Pan, Ting Yao, Hongyang Chao, Tao Mei

Existing works attempt to solve the problem by explicitly imposing uncertainty on classifiers when OOD inputs are exposed to the classifier during training.

Outlier Detection Out-of-Distribution Detection +1

LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception

no code implementations19 Sep 2022 Dongqiangzi Ye, Zixiang Zhou, Weijia Chen, Yufei Xie, Yu Wang, Panqu Wang, Hassan Foroosh

LidarMultiNet is extensively tested on both Waymo Open Dataset and nuScenes dataset, demonstrating for the first time that major LiDAR perception tasks can be unified in a single strong network that is trained end-to-end and achieves state-of-the-art performance.

3D Object Detection 3D Semantic Segmentation +2

Infrared: A Meta Bug Detector

no code implementations18 Sep 2022 Chi Zhang, Yu Wang, Linzhang Wang

The recent breakthroughs in deep learning methods have sparked a wave of interest in learning-based bug detectors.

Anomaly Detection

ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation

1 code implementation27 Aug 2022 Yu Wang, Hengrui Zhang, Zhiwei Liu, Liangwei Yang, Philip S. Yu

Then we propose Contrastive Variational AutoEncoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation.

Contrastive Learning Sequential Recommendation

Ultra Lite Convolutional Neural Network for Fast Automatic Modulation Classification in Low-Resource Scenarios

1 code implementation9 Aug 2022 Lantu Guo, Yu Wang, Yun Lin, Haitao Zhao, Guan Gui

Automatic modulation classification (AMC) is a key technique for designing non-cooperative communication systems, and deep learning (DL) is applied effectively to AMC for improving classification accuracy.

Classification Data Augmentation

BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition

no code implementations7 Aug 2022 Yifan Hu, Yu Wang

However, due to the inconsistent frequency of human activities, the amount of data for each activity in the human activity dataset is imbalanced.

Human Activity Recognition

CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot NAS

1 code implementation16 Jul 2022 Zixuan Zhou, Xuefei Ning, Yi Cai, Jiashu Han, Yiping Deng, Yuhan Dong, Huazhong Yang, Yu Wang

Specifically, we train the supernet with a large sharing extent (an easier curriculum) at the beginning and gradually decrease the sharing extent of the supernet (a harder curriculum).

Neural Architecture Search

Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning

1 code implementation14 Jul 2022 Yu Wang, Guan Gui, Yun Lin, Hsiao-Chun Wu, Chau Yuen, Fumiyuki Adachi

Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel FS-SEI method is proposed, based on deep metric ensemble learning (DMEL).

Ensemble Learning Metric Learning

Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion Recognition

no code implementations11 Jul 2022 Zihan Zhao, Yanfeng Wang, Yu Wang

The research and applications of multimodal emotion recognition have become increasingly popular recently.

Multimodal Emotion Recognition Transfer Learning

Dual Vision Transformer

1 code implementation11 Jul 2022 Ting Yao, Yehao Li, Yingwei Pan, Yu Wang, Xiao-Ping Zhang, Tao Mei

Dual-ViT is henceforth able to reduce the computational complexity without compromising much accuracy.

Class-Specific Semantic Reconstruction for Open Set Recognition

no code implementations5 Jul 2022 Hongzhi Huang, Yu Wang, QinGhua Hu, Ming-Ming Cheng

In this study, we propose a novel method, called Class-Specific Semantic Reconstruction (CSSR), that integrates the power of AE and prototype learning.

Open Set Learning

Collaboration-Aware Graph Convolutional Network for Recommender Systems

1 code implementation3 Jul 2022 Yu Wang, Yuying Zhao, Yi Zhang, Tyler Derr

Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures collaborative effect.

Recommendation Systems

Multi-Granularity Regularized Re-Balancing for Class Incremental Learning

1 code implementation30 Jun 2022 Huitong Chen, Yu Wang, QinGhua Hu

Re-balancing methods are used to alleviate the influence of data imbalance; however, we empirically discover that they would under-fit new classes.

class-incremental learning Class Incremental Learning +1

On Structural Explanation of Bias in Graph Neural Networks

1 code implementation24 Jun 2022 Yushun Dong, Song Wang, Yu Wang, Tyler Derr, Jundong Li

The low transparency on how the structure of the input network influences the bias in GNN outcome largely limits the safe adoption of GNNs in various decision-critical scenarios.

Decision Making Fairness

Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage

1 code implementation7 Jun 2022 Yu Wang, Yuying Zhao, Yushun Dong, Huiyuan Chen, Jundong Li, Tyler Derr

Motivated by our analysis, we propose Fair View Graph Neural Network (FairVGNN) to generate fair views of features by automatically identifying and masking sensitive-correlated features considering correlation variation after feature propagation.

Fairness Representation Learning

Differentiable Invariant Causal Discovery

no code implementations31 May 2022 Yu Wang, An Zhang, Xiang Wang, Yancheng Yuan, Xiangnan He, Tat-Seng Chua

This paper proposes Differentiable Invariant Causal Discovery (DICD), utilizing the multi-environment information based on a differentiable framework to avoid learning spurious edges and wrong causal directions.

Causal Discovery

Efficient Reinforcement Learning from Demonstration Using Local Ensemble and Reparameterization with Split and Merge of Expert Policies

no code implementations23 May 2022 Yu Wang, Fang Liu

The current work on reinforcement learning (RL) from demonstrations often assumes the demonstrations are samples from an optimal policy, an unrealistic assumption in practice.

Continuous Control Reinforcement Learning (RL)

Brachial Plexus Nerve Trunk Segmentation Using Deep Learning: A Comparative Study with Doctors' Manual Segmentation

no code implementations17 May 2022 Yu Wang, Binbin Zhu, Lingsi Kong, Jianlin Wang, Bin Gao, Jianhua Wang, Dingcheng Tian, YuDong Yao

With the help of deep learning methods, the automatic identification or segmentation of nerves can be realized, assisting doctors in completing nerve block anesthesia accurately and efficiently.

BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification

no code implementations14 May 2022 Wenhao Huang, Haifan Gong, huan zhang, Yu Wang, Haofeng Li, Guanbin Li, Hong Shen

CT-based bronchial tree analysis plays an important role in the computer-aided diagnosis for respiratory diseases, as it could provide structured information for clinicians.

Classification Graph Learning +2

Self-Supervised Masking for Unsupervised Anomaly Detection and Localization

no code implementations13 May 2022 Chaoqin Huang, Qinwei Xu, Yanfeng Wang, Yu Wang, Ya zhang

To extend the reconstruction-based anomaly detection architecture to the localized anomalies, we propose a self-supervised learning approach through random masking and then restoring, named Self-Supervised Masking (SSM) for unsupervised anomaly detection and localization.

Defect Detection Medical Diagnosis +2

GypSum: Learning Hybrid Representations for Code Summarization

1 code implementation26 Apr 2022 Yu Wang, Yu Dong, Xuesong Lu, Aoying Zhou

Current deep learning models for code summarization generally follow the principle in neural machine translation and adopt the encoder-decoder framework, where the encoder learns the semantic representations from source code and the decoder transforms the learnt representations into human-readable text that describes the functionality of code snippets.

Code Summarization Graph Attention +3

R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction

no code implementations21 Apr 2022 Yu Wang, Shuo Ye, Shujian Yu, Xinge You

In this paper, we present a novel approach for FGVC, which can simultaneously make use of partial yet sufficient discriminative information in environmental cues and also compress the redundant information in class-token with respect to the target.

Fine-Grained Visual Categorization

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

1 code implementation7 Apr 2022 Zeyu Sun, Monica G. Bobra, Xiantong Wang, Yu Wang, Hu Sun, Tamas Gombosi, Yang Chen, Alfred Hero

We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M- or X-class flare in the future 24 hours, from quiet active regions that do not produce any flare within $\pm 24$ hours.

Primal-dual Estimator Learning: an Offline Constrained Moving Horizon Estimation Method with Feasibility and Near-optimality Guarantees

no code implementations6 Apr 2022 Wenhan Cao, Jingliang Duan, Shengbo Eben Li, Chen Chen, Chang Liu, Yu Wang

Both the primal and dual estimators are learned from data using supervised learning techniques, and the explicit sample size is provided, which enables us to guarantee the quality of each learned estimator in terms of feasibility and optimality.

Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation

1 code implementation19 Mar 2022 Junwen Pan, Pengfei Zhu, Kaihua Zhang, Bing Cao, Yu Wang, Dingwen Zhang, Junwei Han, QinGhua Hu

Semantic segmentation with limited annotations, such as weakly supervised semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS), is a challenging task that has attracted much attention recently.

Pseudo Label Semi-Supervised Semantic Segmentation +2

CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance

no code implementations CVPR 2022 Tianchen Zhao, Niansong Zhang, Xuefei Ning, He Wang, Li Yi, Yu Wang

We propose CodedVTR (Codebook-based Voxel TRansformer), which improves data efficiency and generalization ability for 3D sparse voxel transformers.

3D Semantic Segmentation

Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation

1 code implementation17 Mar 2022 Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Xun Chen, Lichao Sun

Furthermore, we propose a Federated learning paradigm with privacy-preserving Relation embedding aggregation (FedR) to tackle the privacy issue in FedE.

Entity Embeddings Federated Learning +3

A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation

no code implementations13 Feb 2022 Yu Wang, Yarong Ji, Hongbing Xiao

Then the tensor was mapped to a matrix which was used to mix the one-hot encoded labels of the above image patches.

Brain Tumor Segmentation Data Augmentation +1

ChemicalX: A Deep Learning Library for Drug Pair Scoring

1 code implementation10 Feb 2022 Benedek Rozemberczki, Charles Tapley Hoyt, Anna Gogleva, Piotr Grabowski, Klas Karis, Andrej Lamov, Andriy Nikolov, Sebastian Nilsson, Michael Ughetto, Yu Wang, Tyler Derr, Benjamin M Gyori

In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task.

BIG-bench Machine Learning

Evaluating the impact of quarantine measures on COVID-19 spread

no code implementations9 Feb 2022 Renquan Zhang, Yu Wang, Zheng Lv, Sen Pei

We generate counterfactual simulations to estimate effectiveness of quarantine measures.

Decision Making

Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network

1 code implementation7 Feb 2022 Liangwei Yang, Zhiwei Liu, Yu Wang, Chen Wang, Ziwei Fan, Philip S. Yu

We conduct a comprehensive analysis of users' online game behaviors, which motivates the necessity of handling those three characteristics in the online game recommendation.

Recommendation Systems

Improving Out-of-Distribution Robustness via Selective Augmentation

2 code implementations2 Jan 2022 Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, Chelsea Finn

Machine learning algorithms typically assume that training and test examples are drawn from the same distribution.

LAR-SR: A Local Autoregressive Model for Image Super-Resolution

1 code implementation CVPR 2022 Baisong Guo, Xiaoyun Zhang, HaoNing Wu, Yu Wang, Ya zhang, Yan-Feng Wang

Previous super-resolution (SR) approaches often formulate SR as a regression problem and pixel wise restoration, which leads to a blurry and unreal SR output.

Image Super-Resolution

A Style and Semantic Memory Mechanism for Domain Generalization

no code implementations ICCV 2021 Yang Chen, Yu Wang, Yingwei Pan, Ting Yao, Xinmei Tian, Tao Mei

Correspondingly, we also propose a novel "jury" mechanism, which is particularly effective in learning useful semantic feature commonalities among domains.

Domain Generalization

Transferrable Contrastive Learning for Visual Domain Adaptation

no code implementations14 Dec 2021 Yang Chen, Yingwei Pan, Yu Wang, Ting Yao, Xinmei Tian, Tao Mei

From this point, we present a particular paradigm of self-supervised learning tailored for domain adaptation, i. e., Transferrable Contrastive Learning (TCL), which links the SSL and the desired cross-domain transferability congruently.

Contrastive Learning Domain Adaptation +2

Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward

no code implementations12 Dec 2021 Weilin Liu, Ye Mu, Chao Yu, Xuefei Ning, Zhong Cao, Yi Wu, Shuang Liang, Huazhong Yang, Yu Wang

These scenarios indeed correspond to the vulnerabilities of the under-test driving policies, thus are meaningful for their further improvements.

Autonomous Driving Multi-agent Reinforcement Learning

Multiway Ensemble Kalman Filter

1 code implementation8 Dec 2021 Yu Wang, Alfred Hero

In this work, we study the emergence of sparsity and multiway structures in second-order statistical characterizations of dynamical processes governed by partial differential equations (PDEs).

Imbalanced Graph Classification via Graph-of-Graph Neural Networks

2 code implementations1 Dec 2021 Yu Wang, Yuying Zhao, Neil Shah, Tyler Derr

To this end, we introduce a novel framework, Graph-of-Graph Neural Networks (G$^2$GNN), which alleviates the graph imbalance issue by deriving extra supervision globally from neighboring graphs and locally from stochastic augmentations of graphs.

Graph Classification Node Classification

Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking

no code implementations23 Nov 2021 Pengfei Zhu, Hongtao Yu, Kaihua Zhang, Yu Wang, Shuai Zhao, Lei Wang, Tianzhu Zhang, QinGhua Hu

To address this issue, segmentation-based trackers have been proposed that employ per-pixel matching to improve the tracking performance of deformable objects effectively.

Visual Object Tracking Visual Tracking

Learning Dynamic Preference Structure Embedding From Temporal Networks

1 code implementation23 Nov 2021 Tongya Zheng, Zunlei Feng, Yu Wang, Chengchao Shen, Mingli Song, Xingen Wang, Xinyu Wang, Chun Chen, Hao Xu

Our proposed Dynamic Preference Structure (DPS) framework consists of two stages: structure sampling and graph fusion.

Graph Sampling

Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems

1 code implementation NeurIPS 2021 Jiayu Chen, Yuanxin Zhang, Yuanfan Xu, Huimin Ma, Huazhong Yang, Jiaming Song, Yu Wang, Yi Wu

We motivate our paradigm through a variational perspective, where the learning objective can be decomposed into two terms: task learning on the current task distribution, and curriculum update to a new task distribution.

Multi-agent Reinforcement Learning

Meta-learning with an Adaptive Task Scheduler

1 code implementation NeurIPS 2021 Huaxiu Yao, Yu Wang, Ying WEI, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, Chelsea Finn

In ATS, for the first time, we design a neural scheduler to decide which meta-training tasks to use next by predicting the probability being sampled for each candidate task, and train the scheduler to optimize the generalization capacity of the meta-model to unseen tasks.

Drug Discovery Meta-Learning

Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification

1 code implementation22 Oct 2021 Yu Wang, Charu Aggarwal, Tyler Derr

Recent years have witnessed the significant success of applying graph neural networks (GNNs) in learning effective node representations for classification.

Classification Metric Learning +2

Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective

no code implementations18 Oct 2021 Hengrui Zhang, Zhongming Yu, Guohao Dai, Guyue Huang, Yufei Ding, Yuan Xie, Yu Wang

The same data are propagated through the graph structure to perform the same neural operation multiple times in GNNs, leading to redundant computation which accounts for 92. 4% of total operators.

Learning Efficient Multi-Agent Cooperative Visual Exploration

no code implementations12 Oct 2021 Chao Yu, Xinyi Yang, Jiaxuan Gao, Huazhong Yang, Yu Wang, Yi Wu

In this paper, we extend the state-of-the-art single-agent visual navigation method, Active Neural SLAM (ANS), to the multi-agent setting by introducing a novel RL-based planning module, Multi-agent Spatial Planner (MSP). MSP leverages a transformer-based architecture, Spatial-TeamFormer, which effectively captures spatial relations and intra-agent interactions via hierarchical spatial self-attentions.

Reinforcement Learning (RL) Visual Navigation

BoolNet: Streamlining Binary Neural Networks Using Binary Feature Maps

no code implementations29 Sep 2021 Nianhui Guo, Joseph Bethge, Haojin Yang, Kai Zhong, Xuefei Ning, Christoph Meinel, Yu Wang

Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts, often based on specialized model designs using additional 32-bit components.

AutoNF: Automated Architecture Optimization of Normalizing Flows Using a Mixture Distribution Formulation

no code implementations29 Sep 2021 Yu Wang, Jan Drgona, Jiaxin Zhang, Karthik Somayaji NS, Frank Y Liu, Malachi Schram, Peng Li

Although various flow models based on different transformations have been proposed, there still lacks a quantitative analysis of performance-cost trade-offs between different flows as well as a systematic way of constructing the best flow architecture.

Efficient Context-Aware Network for Abdominal Multi-organ Segmentation

1 code implementation22 Sep 2021 Fan Zhang, Yu Wang, Hua Yang

For the context block, we propose strip pooling module to capture anisotropic and long-range contextual information, which exists in abdominal scene.

Organ Segmentation

Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models

1 code implementation28 Aug 2021 Yu Wang, Fang Liu, Daniele E. Schiavazzi

To reduce the computational cost without sacrificing inferential accuracy, we propose Normalizing Flow with Adaptive Surrogate (NoFAS), an optimization strategy that alternatively updates the normalizing flow parameters and surrogate model parameters.

Bayesian Inference Variational Inference

DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN

1 code implementation26 Aug 2021 Yu Wang, Zhiwei Liu, Ziwei Fan, Lichao Sun, Philip S. Yu

In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information.

Knowledge Graphs Recommendation Systems

Tree Decomposed Graph Neural Network

1 code implementation25 Aug 2021 Yu Wang, Tyler Derr

Nevertheless, iterative propagation restricts the information of higher-layer neighborhoods to be transported through and fused with the lower-layer neighborhoods', which unavoidably results in feature smoothing between neighborhoods in different layers and can thus compromise the performance, especially on heterophily networks.

Node Classification Tree Decomposition

A Low Rank Promoting Prior for Unsupervised Contrastive Learning

no code implementations5 Aug 2021 Yu Wang, Jingyang Lin, Qi Cai, Yingwei Pan, Ting Yao, Hongyang Chao, Tao Mei

In this paper, we construct a novel probabilistic graphical model that effectively incorporates the low rank promoting prior into the framework of contrastive learning, referred to as LORAC.

Contrastive Learning Image Classification +5

Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL

no code implementations ACL 2021 Jiaqi Guo, Ziliang Si, Yu Wang, Qian Liu, Ming Fan, Jian-Guang Lou, Zijiang Yang, Ting Liu

However, we identify two biases in existing datasets for XDTS: (1) a high proportion of context-independent questions and (2) a high proportion of easy SQL queries.


MedAI at SemEval-2021 Task 10: Negation-aware Pre-training for Source-free Negation Detection Domain Adaptation

no code implementations SEMEVAL 2021 Jinquan Sun, Qi Zhang, Yu Wang, Lei Zhang

Due to the increasing concerns for data privacy, source-free unsupervised domain adaptation attracts more and more research attention, where only a trained source model is assumed to be available, while the labeled source data remain private.

Negation Detection Source-Free Domain Adaptation +1

United We Learn Better: Harvesting Learning Improvements From Class Hierarchies Across Tasks

1 code implementation28 Jul 2021 Sindi Shkodrani, Yu Wang, Marco Manfredi, Nóra Baka

Attempts of learning from hierarchical taxonomies in computer vision have been mostly focusing on image classification.

Classification Image Classification +2

Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification

no code implementations27 Jul 2021 Yu Wang, Yuesong Shen, Daniel Cremers

To learn the direct influence among output nodes in a graph, we propose the Explicit Pairwise Factorized Graph Neural Network (EPFGNN), which models the whole graph as a partially observed Markov Random Field.

Node Classification

BoolNet: Minimizing The Energy Consumption of Binary Neural Networks

1 code implementation13 Jun 2021 Nianhui Guo, Joseph Bethge, Haojin Yang, Kai Zhong, Xuefei Ning, Christoph Meinel, Yu Wang

Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts.

An Adversarial Learning based Multi-Step Spoken Language Understanding System through Human-Computer Interaction

no code implementations6 Jun 2021 Yu Wang, Yilin Shen, Hongxia Jin

In this paper, we introduce a novel multi-step spoken language understanding system based on adversarial learning that can leverage the multiround user's feedback to update slot values.

Dialogue State Tracking Semantic Frame Parsing +1

A Coarse to Fine Question Answering System based on Reinforcement Learning

no code implementations1 Jun 2021 Yu Wang, Hongxia Jin

In this paper, we present a coarse to fine question answering (CFQA) system based on reinforcement learning which can efficiently processes documents with different lengths by choosing appropriate actions.

Question Answering reinforcement-learning +1

SG-PALM: a Fast Physically Interpretable Tensor Graphical Model

1 code implementation26 May 2021 Yu Wang, Alfred Hero

We propose a new graphical model inference procedure, called SG-PALM, for learning conditional dependency structure of high-dimensional tensor-variate data.

Spatio-Temporal Forecasting

Learning Robust Recommenders through Cross-Model Agreement

no code implementations20 May 2021 Yu Wang, Xin Xin, Zaiqiao Meng, Xiangnan He, Joemon Jose, Fuli Feng

A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference.

Denoising Recommendation Systems

Integrated Communication and Navigation for Ultra-Dense LEO Satellite Networks: Vision, Challenges and Solutions

no code implementations19 May 2021 Yu Wang, Hejia Luo, Ying Chen, Jun Wang, Rong Li, Bin Wang

Next generation beyond 5G networks are expected to provide both Terabits per second data rate communication services and centimeter-level accuracy localization services in an efficient, seamless and cost-effective manner.

Unsupervised Remote Sensing Super-Resolution via Migration Image Prior

1 code implementation8 May 2021 JiaMing Wang, Zhenfeng Shao, Tao Lu, Xiao Huang, Ruiqian Zhang, Yu Wang

Despite their success, however, low/high spatial resolution pairs are usually difficult to obtain in satellites with a high temporal resolution, making such approaches in SR impractical to use.


Domain-Specific Suppression for Adaptive Object Detection

no code implementations CVPR 2021 Yu Wang, Rui Zhang, Shuo Zhang, Miao Li, Yangyang Xia, Xishan Zhang, Shaoli Liu

The directions of weights, and the gradients, can be divided into domain-specific and domain-invariant parts, and the goal of domain adaptation is to concentrate on the domain-invariant direction while eliminating the disturbance from domain-specific one.

Domain Adaptation object-detection +1

Membership Inference Attacks on Knowledge Graphs

no code implementations16 Apr 2021 Yu Wang, Lifu Huang, Philip S. Yu, Lichao Sun

Membership inference attacks (MIAs) infer whether a specific data record is used for target model training.

Inference Attack Knowledge Graph Embedding +3

Adversarial Robustness under Long-Tailed Distribution

1 code implementation CVPR 2021 Tong Wu, Ziwei Liu, Qingqiu Huang, Yu Wang, Dahua Lin

We then perform a systematic study on existing long-tailed recognition methods in conjunction with the adversarial training framework.

Adversarial Robustness

ExKaldi-RT: A Real-Time Automatic Speech Recognition Extension Toolkit of Kaldi

1 code implementation3 Apr 2021 Yu Wang, Chee Siang Leow, Akio Kobayashi, Takehito Utsuro, Hiromitsu Nishizaki

This paper describes the ExKaldi-RT online automatic speech recognition (ASR) toolkit that is implemented based on the Kaldi ASR toolkit and Python language.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Model-Free Learning of Safe yet Effective Controllers

no code implementations26 Mar 2021 Alper Kamil Bozkurt, Yu Wang, Miroslav Pajic

We study the problem of learning safe control policies that are also effective; i. e., maximizing the probability of satisfying a linear temporal logic (LTL) specification of a task, and the discounted reward capturing the (classic) control performance.

reinforcement-learning Reinforcement Learning (RL)

FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning

no code implementations CVPR 2022 Minxue Tang, Xuefei Ning, Yitu Wang, Jingwei Sun, Yu Wang, Hai Li, Yiran Chen

In this work, we propose FedCor -- an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL.

Federated Learning

Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction

2 code implementations13 Mar 2021 Shaowei Chen, Yu Wang, Jie Liu, Yuelin Wang

Aspect sentiment triplet extraction (ASTE), which aims to identify aspects from review sentences along with their corresponding opinion expressions and sentiments, is an emerging task in fine-grained opinion mining.

Aspect Sentiment Triplet Extraction Machine Reading Comprehension +2

Learning-Based Vulnerability Analysis of Cyber-Physical Systems

no code implementations10 Mar 2021 Amir Khazraei, Spencer Hallyburton, Qitong Gao, Yu Wang, Miroslav Pajic

This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS).

Anomaly Detection