Emma Tosch
Artificial Intelligence · 2018–2019
A researcher in the Science corpus: 3 papers; trusts 21 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 42 citations into it; #22,503 of 35,345 by trust here, #29,412 by citations
- Reinforcement Learning — 40 citations into it
- Evaluation & Benchmarks — 10 citations into it
- Optimization & Training — 6 citations into it
Whom Emma Tosch trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- John Schulman
- Volodymyr Mnih
- Richard S. Sutton
- Marc G. Bellemare
- Geoffrey E. Hinton
- Matthew E. Taylor
- Yuhuai Wu
- Diederik P. Kingma
- Alex Graves
- Yoshua Bengio
Who trusts Emma Tosch overall
Papers from Emma Tosch's perspective
Ordered by how strongly the researchers Emma Tosch 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
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Asynchronous Methods for Deep Reinforcement Learning
- Human-level control through deep reinforcement learning
Their papers
- Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments (2019) — 27 citations
- Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning (2019) — 9 citations
- ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents (2018) — 7 citations