activity
20172020
most citedContinual Learning in Generative Adversarial Nets

98 citations · 98 across the 1 of their papers we have counts for

collaborators

11 papers

cs.DB2020

EQL -- an extremely easy to learn knowledge graph query language, achieving highspeed and precise search

Han Liu, Shantao Liu

EQL, also named as Extremely Simple Query Language, can be widely used in the field of knowledge graph, precise search, strong artificial intelligence, database, smart speaker ,pat…

cs.CL2019

T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack

Boxin Wang, Hengzhi Pei, Boyuan Pan +3

Adversarial attacks against natural language processing systems, which perform seemingly innocuous modifications to inputs, can induce arbitrary mistakes to the target models. Thou…

cs.LG2018

Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents

Kaiqing Zhang, Zhuoran Yang, Han Liu +2

Despite the increasing interest in multi-agent reinforcement learning (MARL) in multiple communities, understanding its theoretical foundation has long been recognized as a challen…

cs.LG2018

Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space

Jiechao Xiong, Qing Wang, Zhuoran Yang +7

Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely. Motivated by applications in computer games, we…

cs.LG2018

Fully Implicit Online Learning

Chaobing Song, Ji Liu, Han Liu +2

Regularized online learning is widely used in machine learning applications. In online learning, performing exact minimization ( implicit update) is known to be beneficial t…

cs.AI2018

TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game

Peng Sun, Xinghai Sun, Lei Han +8

Starcraft II (SC2) is widely considered as the most challenging Real Time Strategy (RTS) game. The underlying challenges include a large observation space, a huge (continuous and i…