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Found 6 papers, 2 papers with code
Protein Representation Learning by Geometric Structure Pretraining
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure.
Benchmarking deep generative models for diverse antibody sequence design
Here we consider three recently proposed deep generative frameworks for protein design: (AR) the sequence-based autoregressive generative model, (GVP) the precise structure-based graph neural network, and Fold2Seq that leverages a fuzzy and scale-free representation of a three-dimensional fold, while enforcing structure-to-sequence (and vice versa) consistency.
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm
We apply extreme value theory on the new formal robustness guarantee and the estimated robustness is called second-order CLEVER score.
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World
In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture.
A General Family of Trimmed Estimators for Robust High-dimensional Data Analysis
We consider the problem of robustifying high-dimensional structured estimation.
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality
We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems.
