Austin Tripp

Computational Theory and Mathematics · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 35 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 56 citations into it
• Molecular Biology — 41 citations into it
• Artificial Intelligence — 36 citations into it
• Materials Chemistry — 35 citations into it
• Generative Models — 22 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it
• Environmental Chemistry — 7 citations into it

Whom Austin Tripp trusts overall

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

1. Reuven Y. Rubinstein
2. Michalis K. Titsias
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Jan Peters
6. Yoshua Bengio
7. Alex Krizhevsky
8. Radford M. Neal
9. Yann LeCun
10. José Miguel Hernández-Lobato

Papers from Austin Tripp's perspective

Ordered by how strongly the researchers Austin Tripp 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. GENERATIVE ADVERSARIAL NETS
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining (2020) — 175 citations
• DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design (2021) — 126 citations