Search Results for author:
Found 6 papers, 2 papers with code
Exact and efficient phylodynamic simulation from arbitrarily large populations
Consequently, the computational cost is determined not by the size of the final simulated tree, but by the size of the population tree in which it is embedded.
Parallelizing Contextual Bandits
Standard approaches to decision-making under uncertainty focus on sequential exploration of the space of decisions.
Single Layers of Attention Suffice to Predict Protein Contacts
On the one hand, factored attention is a direct simplification of multihead scaled dot-product attention in the Transformer.
Evaluating Protein Transfer Learning with TAPE
Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation techniques.
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires integrating over a set of correlated, extremely high-dimensional latent variables.
Tensor Decompositions via Two-Mode Higher-Order SVD (HOSVD)
Tensor decompositions have rich applications in statistics and machine learning, and developing efficient, accurate algorithms for the problem has received much attention recently.
