Ben Adlam

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 140 citations into it
• Computer Vision and Pattern Recognition — 46 citations into it
• Scaling & Learning Theory — 40 citations into it
• Optimization & Training — 28 citations into it
• Statistics and Probability — 16 citations into it
• Statistical Learning Theory — 13 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Ben Adlam trusts overall

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

1. David A. McAllester
2. Sanjeev Arora
3. Vladimir Naumovich Vapnik
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Alex Krizhevsky
7. Kaiming He
8. Diederik P. Kingma
9. Behnam Neyshabur
10. Peter L. Bartlett

Papers from Ben Adlam's perspective

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

Their papers

• Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020) — 897 citations
• The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks (2020) — 74 citations
• Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models (2023) — 337 citations