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researcher

Cong Liu

6 papers hereh-index 6725 citations8 works total

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

author position
  • middle author3
  • last author3

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • cs.RO2
  • cs.CL1
  • cs.LG1
same name
  • Cong Liu — 19 papers, h 15
  • Cong Liu — 13 papers, h 8
  • Cong Liu — 10 papers, h 28
  • Cong Liu — 8 papers, h 4
  • Cong Liu — 8 papers, h 3
  • Cong Liu — 7 papers, h 25

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
20192023
most citedEpisodic Training for Domain Generalization

27 citations · 29 across the 6 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.RO2023

R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics

Zexin Li, Aritra Samanta, Yufei Li +3

Autonomous robotic systems, like autonomous vehicles and robotic search and rescue, require efficient on-device training for continuous adaptation of Deep Reinforcement Learning (D…

cs.RO2023

PIMbot: Policy and Incentive Manipulation for Multi-Robot Reinforcement Learning in Social Dilemmas

Shahab Nikkhoo, Zexin Li, Aritra Samanta +2

Recent research has demonstrated the potential of reinforcement learning (RL) in enabling effective multi-robot collaboration, particularly in social dilemmas where robots face a t…

cs.CL2023

Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data

Yufei Li, Xiao Yu, Yanchi Liu +2

Jointly extracting entity pairs and their relations is challenging when working on distantly-supervised data with ambiguous or noisy labels. To mitigate such impact, we propose unc…

cs.LG2023★ 1 cited

Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks

Saptarshi Nath, Christos Peridis, Eseoghene Ben-Iwhiwhu +5

Lifelong learning agents aim to learn multiple tasks sequentially over a lifetime. This involves the ability to exploit previous knowledge when learning new tasks and to avoid forg…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.