The University of Chicago

research

∙ 07/01/2020

Molecular Latent Space Simulators

Small integration time steps limit molecular dynamics (MD) simulations t...

Hythem Sidky, et al. ∙

research

∙ 07/22/2021

QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits

Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (...

Hanrui Wang, et al. ∙

research

∙ 09/07/2021

Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets

Many existing works have made great strides towards reducing racial bias...

Matthew Gwilliam, et al. ∙

research

∙ 03/24/2020

Deformable Style Transfer

Both geometry and texture are fundamental aspects of visual style. Exist...

Sunnie S. Y. Kim, et al. ∙

research

∙ 10/28/2021

V2iFi: in-Vehicle Vital Sign Monitoring via Compact RF Sensing

Given the significant amount of time people spend in vehicles, health is...

Tianyue Zheng, et al. ∙

research

∙ 10/01/2020

VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models

Energy-based models (EBMs) have recently been successful in representing...

Zhisheng Xiao, et al. ∙

research

∙ 12/18/2018

Solving the Empirical Bayes Normal Means Problem with Correlated Noise

The Normal Means problem plays a fundamental role in many areas of moder...

Lei Sun, et al. ∙

research

∙ 04/15/2020

Human Evaluation of Interpretability: The Case of AI-Generated Music Knowledge

Interpretability of machine learning models has gained more and more att...

Haizi Yu, et al. ∙

research

∙ 03/21/2020

ARDA: Automatic Relational Data Augmentation for Machine Learning

Automatic machine learning () is a family of techniques to automate the ...

Nadiia Chepurko, et al. ∙

research

∙ 04/29/2019

Style Transfer by Relaxed Optimal Transport and Self-Similarity

Style transfer algorithms strive to render the content of one image usin...

Nicholas Kolkin, et al. ∙

research

∙ 07/12/2022

Tuning the Geometry of Graph Neural Networks

By recursively summing node features over entire neighborhoods, spatial ...

Sowon Jeong, et al. ∙

research

∙ 03/26/2020

Too many cooks: Coordinating multi-agent collaboration through inverse planning

Collaboration requires agents to coordinate their behavior on the fly, s...

Rose E. Wang, et al. ∙

research

∙ 09/20/2021

"Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World

Advances in deep learning have introduced a new wave of voice synthesis ...

Emily Wenger, et al. ∙

research

∙ 02/09/2019

Nonlinear Discovery of Slow Molecular Modes using Hierarchical Dynamics Encoders

The success of enhanced sampling molecular simulations that accelerate a...

Wei Chen, et al. ∙

research

∙ 08/28/2019

Fingerspelling recognition in the wild with iterative visual attention

Sign language recognition is a challenging gesture sequence recognition ...

Bowen Shi, et al. ∙

research

∙ 04/04/2020

Pixel Consensus Voting for Panoptic Segmentation

The core of our approach, Pixel Consensus Voting, is a framework for ins...

Haochen Wang, et al. ∙

research

∙ 07/30/2020

Growing Efficient Deep Networks by Structured Continuous Sparsification

We develop an approach to training deep networks while dynamically adjus...

Xin Yuan, et al. ∙

research

∙ 10/28/2021

Teaching an Active Learner with Contrastive Examples

We study the problem of active learning with the added twist that the le...

Chaoqi Wang, et al. ∙

research

∙ 11/03/2021

Online Learning of Energy Consumption for Navigation of Electric Vehicles

Energy-efficient navigation constitutes an important challenge in electr...

Niklas Åkerblom, et al. ∙

research

∙ 07/15/2022

Towards Understanding Confusion and Affective States Under Communication Failures in Voice-Based Human-Machine Interaction

We present a series of two studies conducted to understand user's affect...

Sujeong Kim, et al. ∙

research

∙ 09/10/2019

Deep Learning for Automated Classification and Characterization of Amorphous Materials

It is difficult to quantify structure-property relationships and to iden...

Kirk Swanson, et al. ∙

research

∙ 12/04/2019

Domain-independent Dominance of Adaptive Methods

From a simplified analysis of adaptive methods, we derive AvaGrad, a new...

Pedro Savarese, et al. ∙

research

∙ 11/22/2021

Topological Regularization for Dense Prediction

Dense prediction tasks such as depth perception and semantic segmentatio...

Deqing Fu, et al. ∙

research

∙ 02/24/2022

Uncertainty-driven Planner for Exploration and Navigation

We consider the problems of exploration and point-goal navigation in pre...

Georgios Georgakis, et al. ∙

research

∙ 02/18/2020

Is Local SGD Better than Minibatch SGD?

We study local SGD (also known as parallel SGD and federated averaging),...

Blake Woodworth, et al. ∙

research

∙ 12/27/2021

BALanCe: Deep Bayesian Active Learning via Equivalence Class Annealing

Active learning has demonstrated data efficiency in many fields. Existin...

Renyu Zhang, et al. ∙

research

∙ 02/03/2022

Rethinking Explainability as a Dialogue: A Practitioner's Perspective

As practitioners increasingly deploy machine learning models in critical...

Himabindu Lakkaraju, et al. ∙

research

∙ 03/22/2019

Scalable Data Augmentation for Deep Learning

Scalable Data Augmentation (SDA) provides a framework for training deep ...

Yuexi Wang, et al. ∙

research

∙ 07/07/2019

IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery

Materials discovery is crucial for making scientific advances in many do...

Dipendra Jha, et al. ∙

research

∙ 09/30/2019

Generating Diverse Story Continuations with Controllable Semantics

We propose a simple and effective modeling framework for controlled gene...

Lifu Tu, et al. ∙

research

∙ 09/12/2021

"Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding

When reading a literary piece, readers often make inferences about vario...

Faeze Brahman, et al. ∙

research

∙ 04/25/2022

Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation

Despite impressive performance in many benchmark datasets, AI models can...

Vivian Lai, et al. ∙

research

∙ 06/25/2021

Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and Search

Recent work has made significant progress in helping users to automate s...

Junwen Yang, et al. ∙

research

∙ 08/12/2021

Agnostic Online Learning and Excellent Sets

We use algorithmic methods from online learning to revisit a key idea fr...

Maryanthe Malliaris, et al. ∙

research

∙ 03/16/2022

Learning Representation for Bayesian Optimization with Collision-free Regularization

Bayesian optimization has been challenged by datasets with large-scale, ...

Fengxue Zhang, et al. ∙

research

∙ 01/30/2020

Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction

With the recent success and popularity of pre-trained language models (L...

Taeuk Kim, et al. ∙

research

∙ 02/11/2022

Assessing Privacy Risks from Feature Vector Reconstruction Attacks

In deep neural networks for facial recognition, feature vectors are nume...

Emily Wenger, et al. ∙

research

∙ 05/13/2022

A Property Induction Framework for Neural Language Models

To what extent can experience from language contribute to our conceptual...

Kanishka Misra, et al. ∙

research

∙ 11/30/2017

Phase Transitions in Approximate Ranking

We study the problem of approximate ranking from observations of pairwis...

Chao Gao, et al. ∙

research

∙ 08/06/2017

Machine learning in sentiment reconstruction of the simulated stock market

In this paper we continue the study of the simulated stock market framew...

Mikhail Goykhman, et al. ∙

research

∙ 11/14/2017

Stochastic Strictly Contractive Peaceman-Rachford Splitting Method

In this paper, we propose a couple of new Stochastic Strictly Contractiv...

Sen Na, et al. ∙

research

∙ 07/06/2017

RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning

Anonymized electronic medical records are an increasingly popular source...

Ji-Sung Kim, et al. ∙

research

∙ 10/04/2017

Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties

Inferring predictive maps between multiple input and multiple output var...

Ming Yu, et al. ∙

research

∙ 09/14/2017

The Impact of Local Geometry and Batch Size on the Convergence and Divergence of Stochastic Gradient Descent

Stochastic small-batch (SB) methods, such as mini-batch Stochastic Gradi...

Vivak Patel, et al. ∙

research

∙ 09/01/2017

Sparse Regularization in Marketing and Economics

Sparse alpha-norm regularization has many data-rich applications in mark...

Guanhao Feng, et al. ∙

research

∙ 03/20/2016

Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

As the field of data science continues to grow, there will be an ever-in...

Randal S. Olson, et al. ∙

research

∙ 05/31/2017

Bayesian l_0 Regularized Least Squares

Bayesian l_0-regularized least squares provides a variable selection tec...

Nicholas G. Polson, et al. ∙

research

∙ 11/07/2016

Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering

We propose the Gaussian attention model for content-based neural memory ...

Liwen Zhang, et al. ∙

research

∙ 10/09/2016

Interpreting Neural Networks to Improve Politeness Comprehension

We present an interpretable neural network approach to predicting and un...

Malika Aubakirova, et al. ∙

research

∙ 03/22/2017

LogitBoost autoregressive networks

Multivariate binary distributions can be decomposed into products of uni...

Marc Goessling, et al. ∙

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