Matthew Craig Gombolay

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 4 papers; trusts 75 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #28,784 of 35,345 by trust here, #26,682 by citations
• Natural Language Processing — 49 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it
• Safety, Robustness & Interpretability — 31 citations into it; #1,823 of 2,251 by trust here, #2,180 by citations
• Reinforcement Learning — 23 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• General Social Sciences — 5 citations into it; #206 of 274 by trust here, #220 by citations
• Large Language Models — 5 citations into it

Whom Matthew Craig Gombolay trusts overall

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

1. Jeffrey Pennington
2. Geoffrey E. Hinton
3. Tomas Mikolov
4. Richard Socher
5. Yoshua Bengio
6. Jacob Devlin
7. Diederik P. Kingma
8. Aylin Caliskan
9. Alex Krizhevsky
10. Ilya Sutskever

Who trusts Matthew Craig Gombolay overall

1. Vinícius G. Costa
2. Karolina Stańczak

Papers from Matthew Craig Gombolay's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. GloVe: Global Vectors for Word Representation
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• LanCon-Learn: Learning with Language to Enable Generalization in Multi-Task Manipulation (2021) — 40 citations
• Towards a Comprehensive Understanding and Accurate Evaluation of Societal Biases in Pre-Trained Transformers (2021) — 66 citations
• Optimization Methods for Interpretable Differentiable Decision Trees Applied to Reinforcement Learning (2020) — 115 citations
• Cross-Loss Influence Functions to Explain Deep Network Representations (2020) — 20 citations