Search Results for author:
Found 7 papers, 6 papers with code
Fast hyperboloid decision tree algorithms
In response to these challenges, we present hyperDT, a novel extension of decision tree algorithms into hyperbolic space.
Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference
Bayesian phylogenetic inference is often conducted via local or sequential search over topologies and branch lengths using algorithms such as random-walk Markov chain Monte Carlo (MCMC) or Combinatorial Sequential Monte Carlo (CSMC).
Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations
Our dataset consists of amino acid sequences, Q8 secondary structures, position specific scoring matrices, multiple sequence alignment co-evolutionary features, backbone atom distance matrices, torsion angles, and 3D coordinates.
Particle Smoothing Variational Objectives
We apply SVO to three nonlinear latent dynamics tasks and provide statistics to rigorously quantify the predictions of filtered and smoothed objectives.
Smoothing Nonlinear Variational Objectives with Sequential Monte Carlo
The task of recovering nonlinear dynamics and latent structure from a population recording is a challenging problem in statistical neuroscience motivating the development of novel techniques in time series analysis.
High Quality Prediction of Protein Q8 Secondary Structure by Diverse Neural Network Architectures
In the spirit of reproducible research we make our data, models and code available, aiming to set a gold standard for purity of training and testing sets.
Parkinson's Disease Digital Biomarker Discovery with Optimized Transitions and Inferred Markov Emissions
These observations were modeled as a cycle of hidden states with randomness allowing deviation from a canonical pattern of transitions and emissions, under the hypothesis that the averaged features of hidden states would serve to informatively characterize classes of patients/controls.
