Jacob Buckman

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 200 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 139 citations into it; #10,885 of 35,345 by trust here, #7,310 by citations
• Reinforcement Learning — 101 citations into it; #1,261 of 2,242 by trust here, #1,300 by citations
• Computer Vision and Pattern Recognition — 50 citations into it
• Generative Models — 33 citations into it
• Management Science and Operations Research — 24 citations into it
• Optimization & Training — 21 citations into it
• Natural Language Processing — 19 citations into it
• Computational Theory and Mathematics — 16 citations into it

Whom Jacob Buckman trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Richard S. Sutton
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Alex Graves
7. Yann LeCun
8. Kaiming He
9. Ilya Sutskever
10. J. Urgen Schmidhuber

Who trusts Jacob Buckman overall

1. Michael Janner
2. Igor Mordatch
3. Sergey Levine
4. Aravind Rajeswaran
5. Mathias Lécuyer
6. Scott Fujimoto
7. Eric Wong
8. David Stutz
9. Anish Athalye
10. Maria-Irina Nicolae

Papers from Jacob Buckman's perspective

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

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

Their papers

• Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion (2018) — 370 citations
• DeepMDP: Learning Continuous Latent Space Models for Representation Learning (2019) — 351 citations
• Is Generator Conditioning Causally Related to GAN Performance? (2018) — 124 citations
• The Importance of Pessimism in Fixed-Dataset Policy Optimization (2020) — 147 citations
• Neural Lattice Language Models (2018) — 30 citations
• Thermometer Encoding: One Hot Way To Resist Adversarial Examples (2018) — 647 citations