Correcting the Dropout-LayerNorm Expectation Gap Improves Protein Structure Models
q-bio.BM, cs.LG
Submitted: 2026-09-25
Updated: 2026-09-25
Code: https://github.com/oxpig/DLC
Terminology
Sources
- Layer Normalization
- PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences
- CogView: Mastering Text-to-Image Generation via Transformers
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift
- Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds
- Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2
- LLaMA: Open and Efficient Foundation Language Models
- QuickBind: A Light-Weight And Interpretable Molecular Docking Model
- Attention Is All You Need
- Improved motif-scaffolding with SE(3) flow matching
- Root Mean Square Layer Normalization
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