Neel Guha

Artificial Intelligence · 2018–2024

A researcher in the Science corpus: 11 papers; trusts 258 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 246 citations into it; #20,382 of 35,345 by trust here, #1,746 by citations
• Natural Language Processing — 99 citations into it
• Computer Vision and Pattern Recognition — 67 citations into it
• Evaluation & Benchmarks — 66 citations into it
• Large Language Models — 65 citations into it
• Safety, Robustness & Interpretability — 36 citations into it
• Optimization & Training — 29 citations into it
• Representation & Self-Supervised Learning — 21 citations into it

Whom Neel Guha trusts overall

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

1. Alex Krizhevsky
2. Thomas G. Dietterich
3. Geoffrey E. Hinton
4. Yann LeCun
5. Yoshua Bengio
6. H. Brendan McMahan
7. Ian J. Goodfellow
8. Karen Simonyan
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Neel Guha overall

1. Hong-You Chen
2. Zhuangdi Zhu
3. Percy Liang
4. Tian Li
5. Jie Xu
6. Jihong Park
7. Wei-Lun Chao
8. Priyanka Mary Mammen
9. Junyuan Hong
10. Yuta Koreeda

Papers from Neel Guha's perspective

Ordered by how strongly the researchers Neel Guha 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
• Holistic Evaluation of Language Models (2023) — 1,863 citations
• Ask Me Anything: A simple strategy for prompting language models (2022) — 275 citations
• When does pretraining help?: assessing self-supervised learning for law and the CaseHOLD dataset of 53,000+ legal holdings (2021) — 362 citations
• One-Shot Federated Learning (2019) — 268 citations
• When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations
• Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT (2024)
• LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models (2023) — 460 citations
• Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset (2022) — 154 citations
• Knowledge Aggregation via Epsilon Model Spaces (2018)
• Model Aggregation via Good-Enough Model Spaces (2018) — 6 citations