Jack Hessel

Artificial Intelligence · 2018–2024

A researcher in the Science corpus: 19 papers; trusts 1,433 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 723 citations into it; #19,890 of 35,345 by trust here, #3,091 by citations
• Computer Vision and Pattern Recognition — 544 citations into it; #9,044 of 21,575 by trust here, #3,558 by citations
• Natural Language Processing — 337 citations into it; #5,305 of 5,688 by trust here, #1,229 by citations
• Multimodal Learning — 222 citations into it; #666 of 1,211 by trust here, #322 by citations
• Evaluation & Benchmarks — 190 citations into it; #2,580 of 3,147 by trust here, #1,638 by citations
• Large Language Models — 161 citations into it
• Representation & Self-Supervised Learning — 98 citations into it
• Model Architectures — 54 citations into it

Whom Jack Hessel trusts overall

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

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

Who trusts Jack Hessel overall

1. Rowan Zellers
2. Axel Sauer
3. Nupur Kumari
4. Marcella Cornia
5. Ximing Lu
6. Chitwan Saharia
7. Yonatan Bisk
8. Richard Zhang
9. Peter West
10. Xinyin Ma

Papers from Jack Hessel's perspective

Ordered by how strongly the researchers Jack Hessel 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

• MERLOT: Multimodal Neural Script Knowledge Models (2021) — 455 citations
• CLIPScore: A Reference-free Evaluation Metric for Image Captioning (2021) — 3,009 citations
• OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models (2023) — 642 citations
• A Case Study on Combining ASR and Visual Features for Generating Instructional Video Captions (2019) — 40 citations
• Symbolic Knowledge Distillation: from General Language Models to Commonsense Models (2021) — 392 citations
• Quark: Controllable Text Generation with Reinforced Unlearning (2022) — 277 citations
• Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization (2022) — 301 citations
• Reframing Human-AI Collaboration for Generating Free-Text Explanations (2021) — 176 citations
• How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources (2023) — 510 citations
• Does My Multimodal Model Learn Cross-modal Interactions? It’s Harder to Tell than You Might Think! (2020) — 97 citations
• Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets (2018) — 44 citations
• OLMo: Accelerating the Science of Language Models (2024) — 700 citations
• Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features (2019) — 83 citations
• VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use (2023) — 111 citations
• What's "up" with vision-language models? Investigating their struggle with spatial reasoning (2023) — 301 citations
• MERLOT RESERVE: Neural Script Knowledge through Vision and Language and Sound (2022) — 261 citations
• The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning (2022) — 70 citations
• Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer (2021) — 30 citations
• Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTube (2020) — 4 citations