Leo Gao

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 15 papers; trusts 248 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 384 citations into it; #17,445 of 35,345 by trust here, #1,571 by citations
• Natural Language Processing — 178 citations into it; #4,784 of 5,688 by trust here, #3,638 by citations
• Computer Vision and Pattern Recognition — 138 citations into it
• Large Language Models — 111 citations into it; #332 of 799 by trust here, #72 by citations
• Evaluation & Benchmarks — 91 citations into it; #1,848 of 3,147 by trust here, #239 by citations
• Safety, Robustness & Interpretability — 74 citations into it
• Representation & Self-Supervised Learning — 38 citations into it
• Optimization & Training — 34 citations into it

Whom Leo Gao trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Krizhevsky
6. Kaiming He
7. Yann LeCun
8. Christian Szegedy
9. Alec Radford
10. Karen Simonyan

Who trusts Leo Gao overall

1. Stella Biderman
2. Emilio Ferrara
3. Erik Nijkamp
4. Niklas Muennighoff
5. Yasaman Razeghi
6. Ji Lin
7. Angelica Chen
8. Jack Bandy
9. Weijia Shi
10. Luke S. Zettlemoyer

Papers from Leo Gao's perspective

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

Their papers

• Multitask Prompted Training Enables Zero-Shot Task Generalization (2021) — 2,030 citations
• GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow (2021) — 964 citations
• Scaling Laws for Reward Model Overoptimization (2022) — 1,126 citations
• GPT-NeoX-20B: An Open-Source Autoregressive Language Model (2022) — 1,029 citations
• EleutherAI: Going Beyond "Open Science" to "Science in the Open" (2022) — 18 citations
• Scaling and evaluating sparse autoencoders (2024) — 535 citations
• GPT-4 Technical Report (2023) — 26,133 citations
• Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision (2023) — 504 citations
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2023) — 266 citations
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations
• Datasheet for the Pile (2022) — 49 citations
• Cut the CARP: Fishing for zero-shot story evaluation (2021) — 16 citations
• Collaborative Storytelling with Large-scale Neural Language Models (2020) — 61 citations
• The Pile: An 800GB Dataset of Diverse Text for Language Modeling (2020) — 2,920 citations