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Daniel D. Lee

10 papers hereh-index 12590 citations33 works total

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

author position
  • middle author8
  • last author2

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.RO2
  • physics.soc-ph2
  • cs.CV1
  • cs.MA1
same name
  • Daniel D. Lee — 11 papers
  • Daniel D. Lee — 5 papers, h 30
  • Daniel D. Lee — 4 papers
  • Daniel D. Lee — 1 paper, h 15
  • Daniel D. Lee — 1 paper
  • Daniel D. Lee — 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
20172021
most citedLearning to Track Dynamic Targets in Partially Known Environments

8 citations · 21 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2021

Local Disentanglement in Variational Auto-Encoders Using Jacobian L1​ Regularization

Travers Rhodes, Daniel D. Lee

There have been many recent advances in representation learning; however, unsupervised representation learning can still struggle with model identification issues related to rotati…

cs.LG2020★ 8 cited

Learning to Track Dynamic Targets in Partially Known Environments

Heejin Jeong, Hamed Hassani, Manfred Morari +2

We solve active target tracking, one of the essential tasks in autonomous systems, using a deep reinforcement learning (RL) approach. In this problem, an autonomous agent is tasked…

cs.LG2019

Learning Q-network for Active Information Acquisition

Heejin Jeong, Brent Schlotfeldt, Hamed Hassani +3

In this paper, we propose a novel Reinforcement Learning approach for solving the Active Information Acquisition problem, which requires an agent to choose a sequence of actions in…

cs.LG2018

Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients

Arbaaz Khan, Clark Zhang, Daniel D. Lee +2

In this paper, we explore using deep reinforcement learning for problems with multiple agents. Most existing methods for deep multi-agent reinforcement learning consider only a sma…

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