activity
20172020
most citedEvaluating Protein Transfer Learning with TAPE

79 citations · 99 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

5 papers · 1 filter

cs.LG20203 cited

Variable Skipping for Autoregressive Range Density Estimation

Eric Liang, Zongheng Yang, Ion Stoica +3

Deep autoregressive models compute point likelihood estimates of individual data points. However, many applications (i.e., database cardinality estimation) require estimating range…

cs.LG201979 cited

Evaluating Protein Transfer Learning with TAPE

Roshan Rao, Nicholas Bhattacharya, Neil Thomas +5

Protein modeling is an increasingly popular area of machine learning research. Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cos…

cs.LG201917 cited

Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning

Yang Liu, Yunan Luo, Yuanyi Zhong +3

Recent advances in deep reinforcement learning algorithms have shown great potential and success for solving many challenging real-world problems, including Go game and robotic app…

cs.LG2018

Learning from Demonstration in the Wild

Feryal Behbahani, Kyriacos Shiarlis, Xi Chen +8

Learning from demonstration (LfD) is useful in settings where hand-coding behaviour or a reward function is impractical. It has succeeded in a wide range of problems but typically…

cs.LG2017

An Effective Training Method For Deep Convolutional Neural Network

Yang Jiang, Zeyang Dou, Qun Hao +3

In this paper, we propose the nonlinearity generation method to speed up and stabilize the training of deep convolutional neural networks. The proposed method modifies a family of…