activity
20182021
most citedUnderstanding Why Neural Networks Generalize Well Through GSNR of Parameters

10 citations · 20 across the 4 of their papers we have counts for

collaborators

8 papers

cs.CV20218 cited

Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction

Wonkwang Lee, Whie Jung, Han Zhang +6

Learning to predict the long-term future of video frames is notoriously challenging due to inherent ambiguities in the distant future and dramatic amplifications of prediction erro…

cs.LG2021

Cost-Efficient Online Hyperparameter Optimization

Jingkang Wang, Mengye Ren, Ilija Bogunovic +2

Recent work on hyperparameters optimization (HPO) has shown the possibility of training certain hyperparameters together with regular parameters. However, these online HPO algorith…

cs.LG202010 cited

Understanding Why Neural Networks Generalize Well Through GSNR of Parameters

Jinlong Liu, Guoqing Jiang, Yunzhi Bai +2

As deep neural networks (DNNs) achieve tremendous success across many application domains, researchers tried to explore in many aspects on why they generalize well. In this paper,…

cs.LG2019

Differentiable Product Quantization for End-to-End Embedding Compression

Ting Chen, Lala Li, Yizhou Sun

Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings. Despite their effectiveness, the number of parame…

cs.LG20192 cited

Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference

Shun Liao, Ting Chen, Tian Lin +2

Computations for the softmax function are significantly expensive when the number of output classes is large. In this paper, we present a novel softmax inference speedup method, Do…

cs.LG2018

Self-Supervised GAN to Counter Forgetting

Ting Chen, Xiaohua Zhai, Neil Houlsby

GANs involve training two networks in an adversarial game, where each network's task depends on its adversary. Recently, several works have framed GAN training as an online or cont…