Hugh Leather

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 3 papers; trusts 41 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it
• Natural Language Processing — 32 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Information Systems — 19 citations into it
• Large Language Models — 19 citations into it
• Model Architectures — 8 citations into it
• Software — 7 citations into it
• Computer Science Applications — 5 citations into it

Whom Hugh Leather trusts overall

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

1. Miltiadis Allamanis
2. Geoffrey E. Hinton
3. Tomas Mikolov
4. Diederik P. Kingma
5. Yoshua Bengio
6. Ilya Sutskever
7. Alex Graves
8. Oriol Vinyals
9. Veselin Raychev
10. Sepp Hochreiter

Papers from Hugh Leather's perspective

Ordered by how strongly the researchers Hugh Leather 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• End-to-End Deep Learning of Optimization Heuristics (2017) — 214 citations
• Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning (2022)
• Synthesizing benchmarks for predictive modeling (2017) — 121 citations