Search Results for author: Volodymyr Kuleshov

Found 13 papers, 8 papers with code

A Multi-Modal and Multitask Benchmark in the Clinical Domain

no code implementations1 Jan 2021 Yong Huang, Edgar Mariano Marroquin, Volodymyr Kuleshov

Here, we introduce Multi-Modal Multitask MIMIC-III (M3) — a dataset and benchmark for evaluating machine learning algorithms in the healthcare domain.

Time Series

Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.

1 code implementation NeurIPS 2019 Sawyer Birnbaum, Volodymyr Kuleshov, Zayd Enam, Pang Wei W. Koh, Stefano Ermon

Learning representations that accurately capture long-range dependencies in sequential inputs --- including text, audio, and genomic data --- is a key problem in deep learning.

Audio Super-Resolution Super-Resolution +1

Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations

1 code implementation14 Sep 2019 Sawyer Birnbaum, Volodymyr Kuleshov, Zayd Enam, Pang Wei Koh, Stefano Ermon

Learning representations that accurately capture long-range dependencies in sequential inputs -- including text, audio, and genomic data -- is a key problem in deep learning.

Audio Super-Resolution Super-Resolution +1

Calibrated Model-Based Deep Reinforcement Learning

1 code implementation19 Jun 2019 Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon

Estimates of predictive uncertainty are important for accurate model-based planning and reinforcement learning.

Model-based Reinforcement Learning

Adversarial Constraint Learning for Structured Prediction

1 code implementation27 May 2018 Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon

Constraint-based learning reduces the burden of collecting labels by having users specify general properties of structured outputs, such as constraints imposed by physical laws.

Pose Estimation Structured Prediction +2

Adversarial Examples for Natural Language Classification Problems

no code implementations ICLR 2018 Volodymyr Kuleshov, Shantanu Thakoor, Tingfung Lau, Stefano Ermon

Modern machine learning algorithms are often susceptible to adversarial examples — maliciously crafted inputs that are undetectable by humans but that fool the algorithm into producing undesirable behavior.

Classification Fake News Detection +2

Audio Super Resolution using Neural Networks

4 code implementations2 Aug 2017 Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon

We introduce a new audio processing technique that increases the sampling rate of signals such as speech or music using deep convolutional neural networks.

Audio Super-Resolution

Estimating Uncertainty Online Against an Adversary

no code implementations13 Jul 2016 Volodymyr Kuleshov, Stefano Ermon

Assessing uncertainty is an important step towards ensuring the safety and reliability of machine learning systems.

General Classification Medical Diagnosis +1

Calibrated Structured Prediction

1 code implementation NeurIPS 2015 Volodymyr Kuleshov, Percy S. Liang

In user-facing applications, displaying calibrated confidence measures---probabilities that correspond to true frequency---can be as important as obtaining high accuracy.

Medical Diagnosis Optical Character Recognition +2

Tensor Factorization via Matrix Factorization

1 code implementation29 Jan 2015 Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang

Tensor factorization arises in many machine learning applications, such knowledge base modeling and parameter estimation in latent variable models.

Latent Variable Models

Algorithms for multi-armed bandit problems

no code implementations25 Feb 2014 Volodymyr Kuleshov, Doina Precup

Although the design of clinical trials has been one of the principal practical problems motivating research on multi-armed bandits, bandit algorithms have never been evaluated as potential treatment allocation strategies.

Multi-Armed Bandits

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