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06/13/2023
3D molecule generation by denoising voxel grids
We propose a new score-based approach to generate 3D molecules represent...
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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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03/21/2023
Universal Smoothed Score Functions for Generative Modeling
We consider the problem of generative modeling based on smoothing an unk...
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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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12/18/2021
Multimeasurement Generative Models
We formally map the problem of sampling from an unknown distribution wit...
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01/28/2021
Automatic design of novel potential 3CL^pro and PL^pro inhibitors
With the goal of designing novel inhibitors for SARS-CoV-1 and SARS-CoV-...
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07/29/2020
Unnormalized Variational Bayes
We unify empirical Bayes and variational Bayes for approximating unnorma...
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05/18/2020
Learning and Inference in Imaginary Noise Models
Inspired by recent developments in learning smoothed densities with empi...
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05/09/2020
Provable Robust Classification via Learned Smoothed Densities
Smoothing classifiers and probability density functions with Gaussian ke...
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10/28/2019
On approximating ∇ f with neural networks
Consider a feedforward neural network ψ: R^d→R^d such that ψ≈∇ f, where ...
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03/06/2019
Neural Empirical Bayes
We formulate a novel framework that unifies kernel density estimation an...
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05/21/2018
Deep Energy Estimator Networks
Density estimation is a fundamental problem in statistical learning. Thi...
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05/21/2017
Annealed Generative Adversarial Networks
We introduce a novel framework for adversarial training where the target...
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10/27/2015