791 citations · 1.5k across the 57 of their papers we have counts for
10 papers · 1 filter
Deep Human Answer Understanding for Natural Reverse QA
Rujing Yao, Linlin Hou, Lei Yang +3
This study focuses on a reverse question answering (QA) procedure, in which machines proactively raise questions and humans supply the answers. This procedure exists in many real h…
Effective Decoding in Graph Auto-Encoder using Triadic Closure
Han Shi, Haozheng Fan, James T. Kwok
The (variational) graph auto-encoder and its variants have been popularly used for representation learning on graph-structured data. While the encoder is often a powerful graph con…
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS
Han Shi, Renjie Pi, Hang Xu +3
Neural Architecture Search (NAS) has shown great potentials in finding better neural network designs. Sample-based NAS is the most reliable approach which aims at exploring the sea…
Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space
Zac Wellmer, James Kwok
This paper proposes a novel deep reinforcement learning architecture that was inspired by previous tree structured architectures which were only useable in discrete action spaces.…
Efficient Neural Interaction Function Search for Collaborative Filtering
Quanming Yao, Xiangning Chen, James Kwok +2
In collaborative filtering (CF), interaction function (IFC) plays the important role of capturing interactions among items and users. The most popular IFC is the inner product, whi…
Blockwise Adaptivity: Faster Training and Better Generalization in Deep Learning
Shuai Zheng, James T. Kwok
Stochastic methods with coordinate-wise adaptive stepsize (such as RMSprop and Adam) have been widely used in training deep neural networks. Despite their fast convergence, they ca…
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback
Shuai Zheng, Ziyue Huang, James T. Kwok
Communication overhead is a major bottleneck hampering the scalability of distributed machine learning systems. Recently, there has been a surge of interest in using gradient compr…
Efficient Low-Rank Semidefinite Programming with Robust Loss Functions
Quanming Yao, Hangsi Yang, En-Liang Hu +1
In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions. In this paper, we focus on improving the robustness o…
Generalizing from a Few Examples: A Survey on Few-Shot Learning
Yaqing Wang, Quanming Yao, James Kwok +1
Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small. Recently, Few-Shot Learning (FSL) is proposed to tackle…
General Convolutional Sparse Coding with Unknown Noise
Yaqing Wang, James T. Kwok, Lionel M. Ni
Convolutional sparse coding (CSC) can learn representative shift-invariant patterns from multiple kinds of data. However, existing CSC methods can only model noises from Gaussian d…