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09/04/2023
Blind Biological Sequence Denoising with Self-Supervised Set Learning
Biological sequence analysis relies on the ability to denoise the imprec...
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06/01/2023
BOtied: Multi-objective Bayesian optimization with tied multivariate ranks
Many scientific and industrial applications require joint optimization o...
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05/31/2023
Chain of Log-Concave Markov Chains
Markov chain Monte Carlo (MCMC) is a class of general-purpose algorithms...
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02/15/2023
SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers
We investigate Siamese networks for learning related embeddings for augm...
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11/15/2022
Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks
We present a Bayesian graph neural network (BGNN) that can estimate the ...
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10/08/2022
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design
Bayesian optimization offers a sample-efficient framework for navigating...
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05/09/2022
Multi-segment preserving sampling for deep manifold sampler
Deep generative modeling for biological sequences presents a unique chal...
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10/25/2021
Packing 2D disks into a 3D container
In this article, we consider the problem of finding in three dimensions ...
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06/02/2021
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes
Among the most extreme objects in the Universe, active galactic nuclei (...
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11/30/2020
Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant
We investigate the use of approximate Bayesian neural networks (BNNs) in...
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10/26/2020
Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing
In the past few years, approximate Bayesian Neural Networks (BNNs) have ...
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12/11/2018