Diana Borsa

Reinforcement Learning · 2016–2021

A researcher in the Science corpus: 5 papers; trusts 52 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 99 citations into it
• Artificial Intelligence — 94 citations into it; #20,426 of 35,345 by trust here, #14,181 by citations
• Computer Vision and Pattern Recognition — 25 citations into it
• Control and Systems Engineering — 17 citations into it
• Optimization & Training — 13 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Computational Theory and Mathematics — 11 citations into it; #6,787 of 12,182 by trust here, #5,046 by citations
• Management Science and Operations Research — 10 citations into it

Whom Diana Borsa trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Richard S. Sutton
2. Andrew Barto
3. Andrew Y. Ng
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Ronald J. Williams
7. Volodymyr Mnih
8. Pieter Abbeel
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Diana Borsa overall

1. Romain Laroche
2. Andrew M. Saxe
3. Xiaoxiao Guo
4. M. Barlier
5. Ida Momennejad
6. Adam Christopher Earle
7. Shagun Sodhani
8. Amy Zhang
9. Khimya Khetarpal

Papers from Diana Borsa's perspective

Ordered by how strongly the researchers Diana Borsa trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Ray Interference: a Source of Plateaus in Deep Reinforcement Learning (2019) — 79 citations
• Observational Learning by Reinforcement Learning (2017) — 51 citations
• Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement (2018) — 192 citations
• Learning Shared Representations in Multi-task Reinforcement Learning (2016) — 48 citations
• The Option Keyboard: Combining Skills in Reinforcement Learning (2021) — 116 citations