Timothy A. Mann
Artificial Intelligence · 2014–2022
A researcher in the Science corpus: 17 papers; trusts 529 researchers and is trusted by 43 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 368 citations into it; #5,386 of 35,345 by trust here, #3,937 by citations
- Safety, Robustness & Interpretability — 200 citations into it; #980 of 2,251 by trust here, #788 by citations
- Computer Vision and Pattern Recognition — 178 citations into it
- Reinforcement Learning — 125 citations into it; #1,653 of 2,242 by trust here, #1,728 by citations
- Optimization & Training — 57 citations into it
- Signal Processing — 41 citations into it
- Electrical and Electronic Engineering — 31 citations into it; #10,980 of 16,539 by trust here, #6,558 by citations
- Molecular Biology — 31 citations into it
Whom Timothy A. Mann trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Richard S. Sutton
- Andrew Barto
- R´emi Munos
- Geoffrey E. Hinton
- Alex Krizhevsky
- Kaiming He
- Diederik P. Kingma
- Doina Precup
- Yoshua Bengio
- Satinder Singh
Who trusts Timothy A. Mann overall
- Daniel Jaymin Mankowitz
- Shie Mannor
- Gabriel Dulac-Arnold
- Pierre-Luc Bacon
- Doina Precup
- Khimya Khetarpal
- Rodrigo Toro Icarte
- Martin Klissarov
- Lucian Busoniu
- Matthew Riemer
Papers from Timothy A. Mann's perspective
Ordered by how strongly the researchers Timothy A. Mann 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
- 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
- Deep Reinforcement Learning in Large Discrete Action Spaces (2015) — 640 citations
- On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models (2018) — 640 citations
- Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations (2019) — 60 citations
- Adaptive Skills Adaptive Partitions (ASAP) (2016) — 61 citations
- Improving Robustness using Generated Data (2021) — 379 citations
- Off-policy Model-based Learning under Unknown Factored Dynamics (2015) — 28 citations
- Learning Robust Options (2018) — 51 citations
- A Dual Approach to Scalable Verification of Deep Networks (2018) — 416 citations
- Approximate Value Iteration with Temporally Extended Actions (2015) — 44 citations
- Fixing Data Augmentation to Improve Adversarial Robustness (2021) — 319 citations
- Iterative Hierarchical Optimization for Misspecified Problems (IHOMP) (2016) — 10 citations
- MuZero with Self-competition for Rate Control in VP9 Video Compression (2022) — 48 citations
- Data Augmentation Can Improve Robustness (2021) — 411 citations
- Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples (2020) — 378 citations
- Off-policy evaluation for MDPs with unknown structure (2015) — 7 citations
- Time-regularized interrupting options (2014) — 23 citations
- Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations (2014) — 59 citations