Junier B. Oliva | DeepAI

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∙ 07/18/2022

Deeply-Learned Generalized Linear Models with Missing Data

Deep Learning (DL) methods have dramatically increased in popularity in ...

0 David K. Lim, et al. ∙

∙ 06/30/2022

Distribution-based Sketching of Single-Cell Samples

Modern high-throughput single-cell immune profiling technologies, such a...

0 Vishal Athreya Baskaran, et al. ∙

∙ 01/28/2022

Any Variational Autoencoder Can Do Arbitrary Conditioning

Arbitrary conditioning is an important problem in unsupervised learning,...

9 Ryan R. Strauss, et al. ∙

∙ 02/08/2021

Arbitrary Conditional Distributions with Energy

Modeling distributions of covariates, or density estimation, is a core c...

0 Ryan R. Strauss, et al. ∙

∙ 02/05/2021

NRTSI: Non-Recurrent Time Series Imputation for Irregularly-sampled Data

Time series imputation is a fundamental task for understanding time seri...

0 Siyuan Shan, et al. ∙

∙ 01/18/2021

Handling Non-ignorably Missing Features in Electronic Health Records Data Using Importance-Weighted Autoencoders

Electronic Health Records (EHRs) are commonly used to investigate relati...

0 David K. Lim, et al. ∙

∙ 06/07/2020

Deep Goal-Oriented Clustering

Clustering and prediction are two primary tasks in the fields of unsuper...

0 Yifeng Shi, et al. ∙

∙ 09/13/2019

Flow Models for Arbitrary Conditional Likelihoods

Understanding the dependencies among features of a dataset is at the cor...

11 Yang Li, et al. ∙

∙ 02/09/2019

Meta-Curvature

We propose to learn curvature information for better generalization and ...

0 Eunbyung Park, et al. ∙

∙ 01/30/2018

Transformation Autoregressive Networks

The fundamental task of general density estimation has been of keen inte...

0 Junier B. Oliva, et al. ∙

∙ 05/30/2017

Recurrent Estimation of Distributions

This paper presents the recurrent estimation of distributions (RED) for ...

0 Junier B. Oliva, et al. ∙

∙ 03/01/2017

The Statistical Recurrent Unit

Sophisticated gated recurrent neural network architectures like LSTMs an...

0 Junier B. Oliva, et al. ∙

∙ 03/20/2016

Multi-fidelity Gaussian Process Bandit Optimisation

In many scientific and engineering applications, we are tasked with the ...

0 Kirthevasan Kandasamy, et al. ∙

∙ 11/13/2015

Deep Mean Maps

The use of distributions and high-level features from deep architecture ...

0 Junier B. Oliva, et al. ∙

∙ 09/24/2015

Linear-time Learning on Distributions with Approximate Kernel Embeddings

Many interesting machine learning problems are best posed by considering...

0 Dougal J. Sutherland, et al. ∙

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