David D. Jensen

Artificial Intelligence · with Clary · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 102 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it; #26,567 of 35,345 by trust here, #20,377 by citations
• Reinforcement Learning — 50 citations into it
• Statistics and Probability — 33 citations into it
• Natural Language Processing — 29 citations into it
• Causal Inference & Discovery — 28 citations into it
• Safety, Robustness & Interpretability — 24 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Evaluation & Benchmarks — 16 citations into it

Whom David D. Jensen trusts overall

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

1. Donald B. Rubin
2. Geoffrey E. Hinton
3. Volodymyr Mnih
4. Richard S. Sutton
5. Yoshua Bengio
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Victor Veitch
9. Yann LeCun
10. Ilya Sutskever

Who trusts David D. Jensen overall

1. Akanksha Atrey

Papers from David D. Jensen's perspective

Ordered by how strongly the researchers David D. Jensen 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. 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments (2019) — 27 citations
• Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning (2019) — 113 citations
• The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data (2019) — 56 citations
• Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020) — 132 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