Brian L. Trippe

Molecular Biology · 2018–2026

A researcher in the Science corpus: 11 papers; trusts 88 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 118 citations into it
• Generative Models — 61 citations into it
• Artificial Intelligence — 58 citations into it; #9,390 of 35,345 by trust here, #21,316 by citations
• Computer Vision and Pattern Recognition — 53 citations into it
• Probabilistic & Bayesian Methods — 50 citations into it; #762 of 1,525 by trust here, #1,136 by citations
• Statistics and Probability — 24 citations into it; #6,595 of 12,932 by trust here, #10,275 by citations
• Statistical and Nonlinear Physics — 21 citations into it
• Computational Mechanics — 11 citations into it

Whom Brian L. Trippe trusts overall

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

1. Radford M. Neal
2. Diederik P. Kingma
3. Alex Graves
4. Geoffrey E. Hinton
5. Max Welling
6. Yoshua Bengio
7. David John Cameron MacKay
8. Tim Salimans
9. Alex Krizhevsky
10. Jonathan Ho

Who trusts Brian L. Trippe overall

1. Siddharth Swaroop
2. Jiaxin Shi
3. Noel Loo
4. Nick Pawlowski
5. Amir-Hossein Karimi
6. Chen Zeno
7. Yufei Wang
8. Richard E. Turner
9. Lynton Ardizzone
10. Jun Zhu

Papers from Brian L. Trippe's perspective

Ordered by how strongly the researchers Brian L. Trippe 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. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Overpruning in Variational Bayesian Neural Networks (2018) — 55 citations
• Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models (2022) — 200 citations
• Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem (2022) — 347 citations
• De novo design of protein structure and function with RFdiffusion (2023) — 1,299 citations
• Conditional Density Estimation with Bayesian Normalising Flows (2018) — 104 citations
• SE(3) diffusion model with application to protein backbone generation (2023) — 343 citations
• Conductance stable and mechanically durable bi-layer EGaIn composite-coated stretchable fiber for 1D bioelectronics (2023) — 599 citations
• Paclitaxel drives TREM2+ macrophage expansion underlying its inferior therapeutic efficacy compared to Nab-paclitaxel (2026) — 735 citations
• Practical and Asymptotically Exact Conditional Sampling in Diffusion Models (2023) — 230 citations
• Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases (2020) — 305 citations
• The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions (2019) — 30 citations