95 citations · 266 across the 18 of their papers we have counts for
18 papers
Unbiased Knowledge Distillation for Recommendation
Gang Chen, Jiawei Chen, Fuli Feng +2
As a promising solution for model compression, knowledge distillation (KD) has been applied in recommender systems (RS) to reduce inference latency. Traditional solutions first tra…
Where to Look: A Unified Attention Model for Visual Recognition with Reinforcement Learning
Gang Chen
The idea of using the recurrent neural network for visual attention has gained popularity in computer vision community. Although the recurrent attention model (RAM) leverages the g…
Deep Reinforcement Learning based Dynamic Optimization of Bus Timetable
Guanqun Ai, Xingquan Zuo, Gang chen +1
Bus timetable optimization is a key issue to reduce operational cost of bus companies and improve the service quality. Existing methods use exact or heuristic algorithms to optimiz…
Decorrelated Double Q-learning
Gang Chen
Q-learning with value function approximation may have the poor performance because of overestimation bias and imprecise estimate. Specifically, overestimation bias is from the maxi…
Context-aware Active Multi-Step Reinforcement Learning
Gang Chen, Dingcheng Li, Ran Xu
Reinforcement learning has attracted great attention recently, especially policy gradient algorithms, which have been demonstrated on challenging decision making and control tasks.…
A New Framework for Multi-Agent Reinforcement Learning -- Centralized Training and Exploration with Decentralized Execution via Policy Distillation
Gang Chen
Deep reinforcement learning (DRL) is a booming area of artificial intelligence. Many practical applications of DRL naturally involve more than one collaborative learners, making it…
Joint Visual Denoising and Classification using Deep Learning
Gang Chen, Yawei Li, Sargur N. Srihari
Visual restoration and recognition are traditionally addressed in pipeline fashion, i.e. denoising followed by classification. Instead, observing correlations between the two tasks…
Word Recognition with Deep Conditional Random Fields
Gang Chen, Yawei Li, Sargur N. Srihari
Recognition of handwritten words continues to be an important problem in document analysis and recognition. Existing approaches extract hand-engineered features from word images--w…
A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation
Gang Chen
We describe recurrent neural networks (RNNs), which have attracted great attention on sequential tasks, such as handwriting recognition, speech recognition and image to text. Howev…
A Light Sliding-Window Part-of-Speech Tagger for the Apertium Free/Open-Source Machine Translation Platform
Gang Chen, Mikel L. Forcada
This paper describes a free/open-source implementation of the light sliding-window (LSW) part-of-speech tagger for the Apertium free/open-source machine translation platform. First…
Generalized K-fan Multimodal Deep Model with Shared Representations
Gang Chen, Sargur N. Srihari
Multimodal learning with deep Boltzmann machines (DBMs) is an generative approach to fuse multimodal inputs, and can learn the shared representation via Contrastive Divergence (CD)…
Deep Transductive Semi-supervised Maximum Margin Clustering
Gang Chen
Semi-supervised clustering is an very important topic in machine learning and computer vision. The key challenge of this problem is how to learn a metric, such that the instances s…
Deep Learning with Nonparametric Clustering
Gang Chen
Clustering is an essential problem in machine learning and data mining. One vital factor that impacts clustering performance is how to learn or design the data representation (or f…
Sequential Labeling with online Deep Learning
Gang Chen, Ran Xu, Sargur Srihari
Deep learning has attracted great attention recently and yielded the state of the art performance in dimension reduction and classification problems. However, it cannot effectively…
Compositional Structure Learning for Action Understanding
Ran Xu, Gang Chen, Caiming Xiong +2
The focus of the action understanding literature has predominately been classification, how- ever, there are many applications demanding richer action understanding such as mobile…
Restricted Boltzmann Machine for Classification with Hierarchical Correlated Prior
Gang Chen, Sargur H. Srihari
Restricted Boltzmann machines (RBM) and its variants have become hot research topics recently, and widely applied to many classification problems, such as character recognition and…
Latent Fisher Discriminant Analysis
Gang Chen
Linear Discriminant Analysis (LDA) is a well-known method for dimensionality reduction and classification. Previous studies have also extended the binary-class case into multi-clas…
Greedy Multiple Instance Learning via Codebook Learning and Nearest Neighbor Voting
Gang Chen, Jason Corso
Multiple instance learning (MIL) has attracted great attention recently in machine learning community. However, most MIL algorithms are very slow and cannot be applied to large dat…