Sid Black

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 5 papers; trusts 172 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 142 citations into it; #25,676 of 35,345 by trust here, #3,907 by citations
• Computer Vision and Pattern Recognition — 71 citations into it
• Large Language Models — 45 citations into it; #294 of 799 by trust here, #165 by citations
• Natural Language Processing — 42 citations into it
• Multimodal Learning — 22 citations into it; #881 of 1,211 by trust here, #1,076 by citations

Whom Sid Black trusts overall

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

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

Who trusts Sid Black overall

1. Stella Biderman
2. Weijia Shi
3. Frank F. Xu
4. Daphne Ippolito
5. Guilherme Penedo
6. Luke S. Zettlemoyer
7. Teven Le Scao
8. Yasaman Razeghi
9. Erik Nijkamp
10. Kevin Meng

Papers from Sid Black's perspective

Ordered by how strongly the researchers Sid Black 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. 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow (2021) — 964 citations
• GPT-NeoX-20B: An Open-Source Autoregressive Language Model (2022) — 1,029 citations
• When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations
• MAGMA - Multimodal Augmentation of Generative Models through Adapter-based Finetuning (2021) — 112 citations
• The Pile: An 800GB Dataset of Diverse Text for Language Modeling (2020) — 2,920 citations