Natasa Tagasovska | DeepAI

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∙ 06/20/2023

MoleCLUEs: Optimizing Molecular Conformers by Minimization of Differentiable Uncertainty

Structure-based models in the molecular sciences can be highly sensitive...

0 Michael Maser, et al. ∙

∙ 06/01/2023

BOtied: Multi-objective Bayesian optimization with tied multivariate ranks

Many scientific and industrial applications require joint optimization o...

4 Ji-won Park, et al. ∙

∙ 02/24/2023

Retrospective Uncertainties for Deep Models using Vine Copulas

Despite the major progress of deep models as learning machines, uncertai...

0 Natasa Tagasovska, et al. ∙

∙ 11/07/2022

Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling

Latent variable models such as the Variational Auto-Encoder (VAE) have b...

0 Romain Lopez, et al. ∙

∙ 10/19/2022

A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences

Deep generative models have emerged as a popular machine learning-based ...

8 Natasa Tagasovska, et al. ∙

∙ 10/18/2022

Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis

Artificial intelligence (AI) is revolutionizing many areas of our lives,...

0 Yanan Xin, et al. ∙

∙ 04/16/2021

Uncertainty Surrogates for Deep Learning

In this paper we introduce a novel way of estimating prediction uncertai...

0 Radhakrishna Achanta, et al. ∙

∙ 06/12/2019

Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders

We propose a vine copula autoencoder to construct flexible generative mo...

0 Natasa Tagasovska, et al. ∙

∙ 06/12/2019

Deep Smoothing of the Implied Volatility Surface

We present an artificial neural network (ANN) approach to value financia...

0 Damien Ackerer, et al. ∙

∙ 11/30/2018

Generative Models for Simulating Mobility Trajectories

Mobility datasets are fundamental for evaluating algorithms pertaining t...

0 Vaibhav Kulkarni, et al. ∙

∙ 11/02/2018

Frequentist uncertainty estimates for deep learning

We provide frequentist estimates of aleatoric and epistemic uncertainty ...

0 Natasa Tagasovska, et al. ∙

∙ 01/31/2018

Nonparametric Quantile-Based Causal Discovery

Telling cause from effect using observational data is a challenging prob...

0 Natasa Tagasovska, et al. ∙

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