Erik Talvitie
Reinforcement Learning · 2007–2018
A researcher in the Science corpus: 9 papers; trusts 108 researchers and is trusted by 97 researchers here, as inferred from the citation graph.
Subfields
- Reinforcement Learning — 149 citations into it; #229 of 2,242 by trust here, #428 by citations
- Artificial Intelligence — 117 citations into it; #3,824 of 35,345 by trust here, #5,120 by citations
- Management Science and Operations Research — 27 citations into it; #4,307 of 11,946 by trust here, #869 by citations
- Evaluation & Benchmarks — 14 citations into it; #1,379 of 3,147 by trust here, #1,050 by citations
- Computer Vision and Pattern Recognition — 13 citations into it
- Optimization & Training — 13 citations into it
- Computational Theory and Mathematics — 11 citations into it; #2,405 of 12,182 by trust here, #5,046 by citations
- Statistical Learning Theory — 10 citations into it
Whom Erik Talvitie trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Richard S. Sutton
- Stéphane Ross
- Levente Kocsis
- Marc G. Bellemare
- Joel Veness
- Geoffrey E. Hinton
- Ronald J. Williams
- Csaba Szepesvari
- David Silver
- David E. Rumelhart
Who trusts Erik Talvitie overall
- Kavosh Asadi
- Yuxi Li
- Sébastien Racanière
- Marc G. Bellemare
- Michael Janner
- Matthew E. Taylor
- Marlos C. Machado
- G. Holland
- Joel Veness
- Sergey Levine
Papers from Erik Talvitie's perspective
Ordered by how strongly the researchers Erik Talvitie trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Reinforcement Learning: An Introduction
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents (2017) — 623 citations
- State of the Art Control of Atari Games Using Shallow Reinforcement Learning (2015) — 115 citations
- Agnostic System Identification for Monte Carlo Planning (2015) — 18 citations
- The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces (2018) — 44 citations
- Self-Correcting Models for Model-Based Reinforcement Learning (2016) — 104 citations
- Policy Tree: Adaptive Representation for Policy Gradient (2015) — 19 citations
- Model Regularization for Stable Sample Rollouts (2014) — 114 citations
- Skip Context Tree Switching (2014) — 45 citations
- An Experts Algorithm for Transfer Learning (2007) — 52 citations