254 citations · 304 across the 20 of their papers we have counts for
20 papers
Towards Effective Semantic OOD Detection in Unseen Domains: A Domain Generalization Perspective
Haoliang Wang, Chen Zhao, Yunhui Guo +2
Two prevalent types of distributional shifts in machine learning are the covariate shift (as observed across different domains) and the semantic shift (as seen across different cla…
Towards Fair Disentangled Online Learning for Changing Environments
Chen Zhao, Feng Mi, Xintao Wu +4
In the problem of online learning for changing environments, data are sequentially received one after another over time, and their distribution assumptions may vary frequently. Alt…
Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
Aneesh Komanduri, Yongkai Wu, Feng Chen +1
Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information f…
InterFormer: Interactive Local and Global Features Fusion for Automatic Speech Recognition
Zhi-Hao Lai, Tian-Hao Zhang, Qi Liu +5
The local and global features are both essential for automatic speech recognition (ASR). Many recent methods have verified that simply combining local and global features can furth…
Rethinking Speech Recognition with A Multimodal Perspective via Acoustic and Semantic Cooperative Decoding
Tian-Hao Zhang, Hai-Bo Qin, Zhi-Hao Lai +5
Attention-based encoder-decoder (AED) models have shown impressive performance in ASR. However, most existing AED methods neglect to simultaneously leverage both acoustic and seman…
Learning Invariable Semantical Representation from Language for Extensible Policy Generalization
Yihan Li, Jinsheng Ren, Tianrun Xu +3
Recently, incorporating natural language instructions into reinforcement learning (RL) to learn semantically meaningful representations and foster generalization has caught many co…
Adjacency constraint for efficient hierarchical reinforcement learning
Tianren Zhang, Shangqi Guo, Tian Tan +2
Goal-conditioned Hierarchical Reinforcement Learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training in…
Non-autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition
Chuan-Fei Zhang, Yan Liu, Tian-Hao Zhang +3
Non-autoregressive (NAR) transformer models have been studied intensively in automatic speech recognition (ASR), and a substantial part of NAR transformer models is to use the casu…
Fairness-Aware Online Meta-learning
Chen Zhao, Feng Chen, Bhavani Thuraisingham
In contrast to offline working fashions, two research paradigms are devised for online learning: (1) Online Meta Learning (OML) learns good priors over model parameters (or learnin…
Network-wide link travel time and station waiting time estimation using automatic fare collection data: A computational graph approach
Jinlei Zhang, Feng Chen, Lixing Yang +3
Urban rail transit (URT) system plays a dominating role in many megacities like Beijing and Hong Kong. Due to its important role and complex nature, it is always in great need for…
A method of supervised learning from conflicting data with hidden contexts
Tianren Zhang, Yizhou Jiang, Feng Chen
Conventional supervised learning assumes a stable input-output relationship. However, this assumption fails in open-ended training settings where the input-output relationship depe…
CRIL: Continual Robot Imitation Learning via Generative and Prediction Model
Chongkai Gao, Haichuan Gao, Shangqi Guo +2
Imitation learning (IL) algorithms have shown promising results for robots to learn skills from expert demonstrations. However, they need multi-task demonstrations to be provided a…
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang +7
Deep learning's performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does…
A Nested Bi-level Optimization Framework for Robust Few Shot Learning
Krishnateja Killamsetty, Changbin Li, Chen Zhao +2
Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal. Hence, i…
End-to-end trainable network for degraded license plate detection via vehicle-plate relation mining
Song-Lu Chen, Shu Tian, Jia-Wei Ma +4
License plate detection is the first and essential step of the license plate recognition system and is still challenging in real applications, such as on-road scenarios. In particu…
How Out-of-Distribution Data Hurts Semi-Supervised Learning
Xujiang Zhao, Killamsetty Krishnateja, Rishabh Iyer +1
Recent semi-supervised learning algorithms have demonstrated greater success with higher overall performance due to better-unlabeled data representations. Nonetheless, recent resea…
Fair Meta-Learning For Few-Shot Classification
Chen Zhao, Changbin Li, Jincheng Li +1
Artificial intelligence nowadays plays an increasingly prominent role in our life since decisions that were once made by humans are now delegated to automated systems. A machine le…
Unfairness Discovery and Prevention For Few-Shot Regression
Chen Zhao, Feng Chen
We study fairness in supervised few-shot meta-learning models that are sensitive to discrimination (or bias) in historical data. A machine learning model trained based on biased da…
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang +7
Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing. As an…
Deep-learning Architecture for Short-term Passenger Flow Forecasting in Urban Rail Transit
Jinlei Zhang, Feng Chen, Zhiyong Cui +2
Short-term passenger flow forecasting is an essential component in urban rail transit operation. Emerging deep learning models provide good insight into improving prediction precis…