research
∙
09/06/2023
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks
Bayesian inference for neural networks, or Bayesian deep learning, has t...
share
research
∙
06/08/2023
Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian
We consider the prediction of the Hamiltonian matrix, which finds use in...
research
∙
05/06/2023
A Latent Diffusion Model for Protein Structure Generation
Proteins are complex biomolecules that perform a variety of crucial func...
research
∙
01/03/2023
Comprehensive analysis of gene expression profiles to radiation exposure reveals molecular signatures of low-dose radiation response
There are various sources of ionizing radiation exposure, where medical ...
research
∙
07/23/2022
Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data
Medical events of interest, such as mortality, often happen at a low rat...
research
∙
03/31/2022
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition
In many machine learning tasks, input features with varying degrees of p...
research
∙
03/15/2022
MoReL: Multi-omics Relational Learning
Multi-omics data analysis has the potential to discover hidden molecular...
research
∙
10/11/2021
COVID-Datathon: Biomarker identification for COVID-19 severity based on BALF scRNA-seq data
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emergen...
research
∙
10/05/2021
Adaptive Group Testing with Mismatched Models
Accurate detection of infected individuals is one of the critical steps ...
research
∙
09/23/2021
Optimal Decision Making in High-Throughput Virtual Screening Pipelines
Effective selection of the potential candidates that meet certain condit...
research
∙
09/05/2021
Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning
Classification has been a major task for building intelligent systems as...
research
∙
04/04/2021
SimCD: Simultaneous Clustering and Differential expression analysis for single-cell transcriptomic data
Single-Cell RNA sequencing (scRNA-seq) measurements have facilitated gen...
research
∙
03/26/2021
Geometric Affinity Propagation for Clustering with Network Knowledge
Clustering data into meaningful subsets is a major task in scientific da...
research
∙
03/06/2021
Contextual Dropout: An Efficient Sample-Dependent Dropout Module
Dropout has been demonstrated as a simple and effective module to not on...
research
∙
10/12/2020
BayReL: Bayesian Relational Learning for Multi-omics Data Integration
High-throughput molecular profiling technologies have produced high-dime...
research
∙
10/07/2020
Quantifying the multi-objective cost of uncertainty
Various real-world applications involve modeling complex systems with im...
research
∙
06/11/2020
NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Machine learning (ML) systems often encounter Out-of-Distribution (OoD) ...
research
∙
06/11/2020
Probabilistic Best Subset Selection via Gradient-Based Optimization
In high-dimensional statistics, variable selection is an optimization pr...
research
∙
06/11/2020
Probabilistic Best Subset Selection by Gradient-Based Optimization
In high-dimensional statistics, variable selection is an optimization pr...
research
∙
06/07/2020
Bayesian Graph Neural Networks with Adaptive Connection Sampling
We propose a unified framework for adaptive connection sampling in graph...
research
∙
05/21/2020
Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator
Semantic hashing has become a crucial component of fast similarity searc...
research
∙
02/12/2020
Learnable Bernoulli Dropout for Bayesian Deep Learning
In this work, we propose learnable Bernoulli dropout (LBD), a new model-...
research
∙
11/01/2019
ARSM Gradient Estimator for Supervised Learning to Rank
We propose a new model for supervised learning to rank. In our model, th...
research
∙
10/28/2019
Semi-Implicit Stochastic Recurrent Neural Networks
Stochastic recurrent neural networks with latent random variables of com...
research
∙
08/26/2019
Variational Graph Recurrent Neural Networks
Representation learning over graph structured data has been mostly studi...
research
∙
08/19/2019
Semi-Implicit Graph Variational Auto-Encoders
Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to ex...
research
∙
08/01/2019
Bayesian Gamma-Negative Binomial Modeling of Single-Cell RNA Sequencing Data
Background: Single-cell RNA sequencing (scRNA-seq) is a powerful profili...
research
∙
05/15/2019
Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images
Segmentation of ultra-high resolution images is increasingly demanded, y...
research
∙
02/26/2019
Optimal Clustering with Missing Values
Missing values frequently arise in modern biomedical studies due to vari...
research
∙
02/08/2019
Scalable optimal Bayesian classification of single-cell trajectories under regulatory model uncertainty
Single-cell gene expression measurements offer opportunities in deriving...
research
∙
01/08/2019
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models
Emerging wearable sensors have enabled the unprecedented ability to cont...
research
∙
12/18/2018
Fast Exact Computation of Expected HyperVolume Improvement
In multi-objective Bayesian optimization and surrogate-based evolutionar...
research
∙
10/22/2018
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data
Precision medicine aims for personalized prognosis and therapeutics by u...
research
∙
06/15/2018
Safe Active Feature Selection for Sparse Learning
We present safe active incremental feature selection (SAIF) to scale up ...
research
∙
05/03/2018
Experimental Design via Generalized Mean Objective Cost of Uncertainty
The mean objective cost of uncertainty (MOCU) quantifies the performance...
research
∙
03/07/2018
Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain
Next-generation sequencing (NGS) to profile temporal changes in living s...
research
∙
01/02/2018
Optimal Bayesian Transfer Learning
Transfer learning has recently attracted significant research attention,...
research
∙
02/12/2016