Jesse Thomason

Artificial Intelligence · 2014–2022

A researcher in the Science corpus: 16 papers; trusts 989 researchers and is trusted by 90 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 408 citations into it; #7,314 of 35,345 by trust here, #2,953 by citations
• Computer Vision and Pattern Recognition — 356 citations into it; #3,574 of 21,575 by trust here, #2,240 by citations
• Natural Language Processing — 286 citations into it; #1,497 of 5,688 by trust here, #737 by citations
• Multimodal Learning — 136 citations into it; #372 of 1,211 by trust here, #423 by citations
• Reinforcement Learning — 111 citations into it; #2,017 of 2,242 by trust here, #1,455 by citations
• Representation & Self-Supervised Learning — 60 citations into it
• Evaluation & Benchmarks — 57 citations into it
• Model Architectures — 39 citations into it

Whom Jesse Thomason trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Karen Simonyan
5. Diederik P. Kingma
6. Yoshua Bengio
7. Ilya Sutskever
8. Ross B. Girshick
9. Yann LeCun
10. Andrew Zisserman

Who trusts Jesse Thomason overall

1. Mohit Shridhar
2. Yonatan Bisk
3. Subhashini Venugopalan
4. Jeff Donahue
5. Marcus Rohrbach
6. Dieter Fox
7. Valts Blukis
8. Daniel Gordon
9. Alessandro Suglia
10. Aditya Mogadala

Papers from Jesse Thomason's perspective

Ordered by how strongly the researchers Jesse Thomason 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks (2019) — 1,140 citations
• Experience Grounds Language (2020) — 440 citations
• ProgPrompt: Generating Situated Robot Task Plans using Large Language Models (2022) — 1,032 citations
• Shifting the Baseline: Single Modality Performance on Visual Navigation & QA (2018) — 85 citations
• Vision-and-Dialog Navigation (2019) — 405 citations
• Embodied BERT: A Transformer Model for Embodied, Language-guided Visual Task Completion (2021) — 90 citations
• Language Grounding with 3D Objects (2021) — 56 citations
• Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog (2020) — 60 citations
• Prospection: Interpretable plans from language by predicting the future (2019) — 51 citations
• Guiding Exploratory Behaviors for Multi-Modal Grounding of Linguistic Descriptions (2018) — 35 citations
• CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks (2022) — 94 citations
• VAuLT: Augmenting the Vision-and-Language Transformer with the Propagation of Deep Language Representations (2022) — 12 citations
• Opportunistic Active Learning for Grounding Natural Language Descriptions (2017) — 49 citations
• Learning Multi-Modal Grounded Linguistic Semantics by Playing "I Spy" (2016) — 104 citations
• Learning to Interpret Natural Language Commands through Human-Robot Dialog (2015) — 200 citations
• Integrating Language and Vision to Generate Natural Language Descriptions of Videos in the Wild (2014) — 201 citations