Samuel Kessler
Artificial Intelligence · 2019–2021
A researcher in the Science corpus: 3 papers; trusts 84 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 116 citations into it
- Computer Vision and Pattern Recognition — 46 citations into it
- Reinforcement Learning — 33 citations into it
- Meta & Continual Learning — 32 citations into it
- Representation & Self-Supervised Learning — 27 citations into it
- Probabilistic & Bayesian Methods — 24 citations into it
- Optimization & Training — 18 citations into it
- Management Science and Operations Research — 12 citations into it
Whom Samuel Kessler trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Radford M. Neal
- Yoshua Bengio
- Alex Krizhevsky
- Yann LeCun
- Jimmy Ba
- Michael I. Jordan
- Marco Cote
- Alex Graves
Who trusts Samuel Kessler overall
- Sanyam Kapoor
- Nikhil Mehta
- Liyuan Wang
- Ervine Zheng
- Theofanis Karaletsos
- Kevin J Liang
- Xingxing Zhang
Papers from Samuel Kessler's perspective
Ordered by how strongly the researchers Samuel Kessler trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- 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
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Same State, Different Task: Continual Reinforcement Learning without Interference (2021) — 62 citations
- Hierarchical Indian buffet neural networks for Bayesian continual learning (2019) — 26 citations
- Indian Buffet Neural Networks for Continual Learning (2019) — 6 citations