409 citations · 794 across the 7 of their papers we have counts for
8 papers
HashEncoding: Autoencoding with Multiscale Coordinate Hashing
Lukas Zhornyak, Zhengjie Xu, Haoran Tang +1
We present HashEncoding, a novel autoencoding architecture that leverages a non-parametric multiscale coordinate hash function to facilitate a per-pixel decoder without convolution…
WEKA-Based: Key Features and Classifier for French of Five Countries
Zeqian Li, Keyu Qiu, Chenxu Jiao +2
This paper describes a French dialect recognition system that will appropriately distinguish between different regional French dialects. A corpus of five regions - Monaco, French-s…
Is Self-Supervised Learning More Robust Than Supervised Learning?
Yuanyi Zhong, Haoran Tang, Junkun Chen +2
Self-supervised contrastive learning is a powerful tool to learn visual representation without labels. Prior work has primarily focused on evaluating the recognition accuracy of va…
Shuffle Augmentation of Features from Unlabeled Data for Unsupervised Domain Adaptation
Changwei Xu, Jianfei Yang, Haoran Tang +3
Unsupervised Domain Adaptation (UDA), a branch of transfer learning where labels for target samples are unavailable, has been widely researched and developed in recent years with t…
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
Ofir Nachum, Haoran Tang, Xingyu Lu +3
Hierarchical reinforcement learning has demonstrated significant success at solving difficult reinforcement learning (RL) tasks. Previous works have motivated the use of hierarchy…
Modular Architecture for StarCraft II with Deep Reinforcement Learning
Dennis Lee, Haoran Tang, Jeffrey O Zhang +3
We present a novel modular architecture for StarCraft II AI. The architecture splits responsibilities between multiple modules that each control one aspect of the game, such as bui…
Reinforcement Learning with Deep Energy-Based Policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel +1
We propose a method for learning expressive energy-based policies for continuous states and actions, which has been feasible only in tabular domains before. We apply our method to…
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Haoran Tang, Rein Houthooft, Davis Foote +6
Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov d…