Jeremy Wohlwend

Artificial Intelligence · 2019–2025

A researcher in the Science corpus: 5 papers; trusts 158 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #15,259 of 35,345 by trust here, #19,439 by citations
• Computer Vision and Pattern Recognition — 50 citations into it; #19,603 of 21,575 by trust here, #21,103 by citations
• Natural Language Processing — 46 citations into it; #5,122 of 5,688 by trust here, #5,593 by citations
• Molecular Biology — 37 citations into it
• Efficiency & Compression — 32 citations into it
• Model Architectures — 22 citations into it
• Large Language Models — 16 citations into it
• Computational Biology & Bioinformatics — 15 citations into it

Whom Jeremy Wohlwend trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Oriol Vinyals
7. Yann LeCun
8. Kaiming He
9. Jimmy Ba
10. Alex Graves

Who trusts Jeremy Wohlwend overall

1. J. Scott McCarley
2. Gabriel Recchia
3. Xinyin Ma
4. Mengzhou Xia
5. François Lagunas
6. Mojtaba Valipour
7. Xiao Zhou
8. Zi-Mei Yao
9. Arthur Conmy
10. Sehoon Kim

Papers from Jeremy Wohlwend's perspective

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

Their papers

• Structured Pruning of Large Language Models (2019) — 354 citations
• Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction (2025) — 481 citations
• Boltz-1 Democratizing Biomolecular Interaction Modeling (2024) — 426 citations
• Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design (2021) — 172 citations
• Autoregressive Knowledge Distillation through Imitation Learning (2020) — 72 citations