◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Cheng Wu

12 papers hereh-index 385.6k citations162 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author9

Across the 12 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • cs.DC3
  • cs.LG3
  • cs.RO2
  • eess.SY1
same name
  • Cheng Wu — 6 papers, h 7
  • Cheng Wu — 5 papers, h 8
  • Cheng Wu — 2 papers, h 3
  • Cheng Wu — 2 papers, h 2
  • Cheng Wu — 2 papers, h 1
  • Cheng Wu — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152021
most citedImplicit Semantic Data Augmentation for Deep Networks

57 citations · 121 across the 12 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.LG2019

Multi Pseudo Q-learning Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles

Wenjie Shi, Shiji Song, Cheng Wu +1

This paper investigates trajectory tracking problem for a class of underactuated autonomous underwater vehicles (AUVs) with unknown dynamics and constrained inputs. Different from…

cs.LG2019★ 4 cited

Soft Policy Gradient Method for Maximum Entropy Deep Reinforcement Learning

Wenjie Shi, Shiji Song, Cheng Wu

Maximum entropy deep reinforcement learning (RL) methods have been demonstrated on a range of challenging continuous tasks. However, existing methods either suffer from severe inst…

cs.CV2019★ 57 cited

Implicit Semantic Data Augmentation for Deep Networks

Yulin Wang, Xuran Pan, Shiji Song +3

In this paper, we propose a novel implicit semantic data augmentation (ISDA) approach to complement traditional augmentation techniques like flipping, translation or rotation. Our…

cs.LG2019★ 4 cited

Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning

Wenjie Shi, Shiji Song, Hui Wu +3

Model-free deep reinforcement learning (RL) algorithms have been widely used for a range of complex control tasks. However, slow convergence and sample inefficiency remain challeng…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.