The University of Chicago

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Student Government is the main representative body for all graduate and undergraduate students at UChicago. It disburses more than $2 million each year in support of student life.

Molecular Latent Space Simulators

Small integration time steps limit molecular dynamics (MD) simulations t...
07/01/2020 ∙ by Hythem Sidky, et al. ∙ 71 ∙ share

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QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits

Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (...
07/22/2021 ∙ by Hanrui Wang, et al. ∙ 40 ∙ share

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Deformable Style Transfer

Both geometry and texture are fundamental aspects of visual style. Exist...
03/24/2020 ∙ by Sunnie S. Y. Kim, et al. ∙ 26 ∙ share

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

Energy-based models (EBMs) have recently been successful in representing...
10/01/2020 ∙ by Zhisheng Xiao, et al. ∙ 23 ∙ share

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Solving the Empirical Bayes Normal Means Problem with Correlated Noise

The Normal Means problem plays a fundamental role in many areas of moder...
12/18/2018 ∙ by Lei Sun, et al. ∙ 20 ∙ share

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

Interpretability of machine learning models has gained more and more att...
04/15/2020 ∙ by Haizi Yu, et al. ∙ 20 ∙ share

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ARDA: Automatic Relational Data Augmentation for Machine Learning

Automatic machine learning () is a family of techniques to automate the ...
03/21/2020 ∙ by Nadiia Chepurko, et al. ∙ 17 ∙ share

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Style Transfer by Relaxed Optimal Transport and Self-Similarity

Style transfer algorithms strive to render the content of one image usin...
04/29/2019 ∙ by Nicholas Kolkin, et al. ∙ 16 ∙ share

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

Collaboration requires agents to coordinate their behavior on the fly, s...
03/26/2020 ∙ by Rose E. Wang, et al. ∙ 11 ∙ share

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Nonlinear Discovery of Slow Molecular Modes using Hierarchical Dynamics Encoders

The success of enhanced sampling molecular simulations that accelerate a...
02/09/2019 ∙ by Wei Chen, et al. ∙ 10 ∙ share

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Fingerspelling recognition in the wild with iterative visual attention

Sign language recognition is a challenging gesture sequence recognition ...
08/28/2019 ∙ by Bowen Shi, et al. ∙ 10 ∙ share

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Pixel Consensus Voting for Panoptic Segmentation

The core of our approach, Pixel Consensus Voting, is a framework for ins...
04/04/2020 ∙ by Haochen Wang, et al. ∙ 9 ∙ share

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Growing Efficient Deep Networks by Structured Continuous Sparsification

We develop an approach to training deep networks while dynamically adjus...
07/30/2020 ∙ by Xin Yuan, et al. ∙ 9 ∙ share

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Deep Learning for Automated Classification and Characterization of Amorphous Materials

It is difficult to quantify structure-property relationships and to iden...
09/10/2019 ∙ by Kirk Swanson, et al. ∙ 6 ∙ share

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Domain-independent Dominance of Adaptive Methods

From a simplified analysis of adaptive methods, we derive AvaGrad, a new...
12/04/2019 ∙ by Pedro Savarese, et al. ∙ 6 ∙ share

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Is Local SGD Better than Minibatch SGD?

We study local SGD (also known as parallel SGD and federated averaging),...
02/18/2020 ∙ by Blake Woodworth, et al. ∙ 5 ∙ share

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Scalable Data Augmentation for Deep Learning

Scalable Data Augmentation (SDA) provides a framework for training deep ...
03/22/2019 ∙ by Yuexi Wang, et al. ∙ 4 ∙ share

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

Materials discovery is crucial for making scientific advances in many do...
07/07/2019 ∙ by Dipendra Jha, et al. ∙ 4 ∙ share

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Generating Diverse Story Continuations with Controllable Semantics

We propose a simple and effective modeling framework for controlled gene...
09/30/2019 ∙ by Lifu Tu, et al. ∙ 4 ∙ share

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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...
01/30/2020 ∙ by Taeuk Kim, et al. ∙ 2 ∙ share

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Phase Transitions in Approximate Ranking

We study the problem of approximate ranking from observations of pairwis...
11/30/2017 ∙ by Chao Gao, et al. ∙ 0 ∙ share

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Machine learning in sentiment reconstruction of the simulated stock market

In this paper we continue the study of the simulated stock market framew...
08/06/2017 ∙ by Mikhail Goykhman, et al. ∙ 0 ∙ share

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Stochastic Strictly Contractive Peaceman-Rachford Splitting Method

In this paper, we propose a couple of new Stochastic Strictly Contractiv...
11/14/2017 ∙ by Sen Na, et al. ∙ 0 ∙ share

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

Anonymized electronic medical records are an increasingly popular source...
07/06/2017 ∙ by Ji-Sung Kim, et al. ∙ 0 ∙ share

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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...
10/04/2017 ∙ by Ming Yu, et al. ∙ 0 ∙ share

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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...
09/14/2017 ∙ by Vivak Patel, et al. ∙ 0 ∙ share

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Sparse Regularization in Marketing and Economics

Sparse alpha-norm regularization has many data-rich applications in mark...
09/01/2017 ∙ by Guanhao Feng, et al. ∙ 0 ∙ share

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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...
03/20/2016 ∙ by Randal S. Olson, et al. ∙ 0 ∙ share

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Bayesian l_0 Regularized Least Squares

Bayesian l_0-regularized least squares provides a variable selection tec...
05/31/2017 ∙ by Nicholas G. Polson, et al. ∙ 0 ∙ share

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

We propose the Gaussian attention model for content-based neural memory ...
11/07/2016 ∙ by Liwen Zhang, et al. ∙ 0 ∙ share

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Interpreting Neural Networks to Improve Politeness Comprehension

We present an interpretable neural network approach to predicting and un...
10/09/2016 ∙ by Malika Aubakirova, et al. ∙ 0 ∙ share

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LogitBoost autoregressive networks

Multivariate binary distributions can be decomposed into products of uni...
03/22/2017 ∙ by Marc Goessling, et al. ∙ 0 ∙ share

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A unified treatment of multiple testing with prior knowledge using the p-filter

A significant literature studies ways of employing prior knowledge to im...
03/18/2017 ∙ by Aaditya Ramdas, et al. ∙ 0 ∙ share

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Stochastic Canonical Correlation Analysis

We tightly analyze the sample complexity of CCA, provide a learning algo...
02/21/2017 ∙ by Chao Gao, et al. ∙ 0 ∙ share

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Dynamic Partition Models

We present a new approach for learning compact and intuitive distributed...
02/16/2017 ∙ by Marc Goessling, et al. ∙ 0 ∙ share

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On SGD's Failure in Practice: Characterizing and Overcoming Stalling

Stochastic Gradient Descent (SGD) is widely used in machine learning pro...
02/01/2017 ∙ by Vivak Patel, et al. ∙ 0 ∙ share

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Colorization as a Proxy Task for Visual Understanding

We investigate and improve self-supervision as a drop-in replacement for...
03/11/2017 ∙ by Gustav Larsson, et al. ∙ 0 ∙ share

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A Geometrical Approach to Topic Model Estimation

In the probabilistic topic models, the quantity of interest---a low-rank...
08/16/2016 ∙ by Zheng Tracy Ke, et al. ∙ 0 ∙ share

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Ensemble preconditioning for Markov chain Monte Carlo simulation

We describe parallel Markov chain Monte Carlo methods that propagate a c...
07/13/2016 ∙ by Charles Matthews, et al. ∙ 0 ∙ share

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Machine Learning for Antimicrobial Resistance

Biological datasets amenable to applied machine learning are more availa...
07/05/2016 ∙ by John W. Santerre, et al. ∙ 0 ∙ share

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Jointly Learning Multiple Measures of Similarities from Triplet Comparisons

Similarity between objects is multi-faceted and it can be easier for hum...
03/05/2015 ∙ by Liwen Zhang, et al. ∙ 0 ∙ share

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Completely random measures for modeling power laws in sparse graphs

Network data appear in a number of applications, such as online social n...
03/22/2016 ∙ by Diana Cai, et al. ∙ 0 ∙ share

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Edge-exchangeable graphs and sparsity

A known failing of many popular random graph models is that the Aldous-H...
03/22/2016 ∙ by Tamara Broderick, et al. ∙ 0 ∙ share

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Identifiability of an X-rank decomposition of polynomial maps

In this paper, we study a polynomial decomposition model that arises in ...
03/04/2016 ∙ by Pierre Comon, et al. ∙ 0 ∙ share

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Examining the Impact of Blur on Recognition by Convolutional Networks

State-of-the-art algorithms for many semantic visual tasks are based on ...
11/17/2016 ∙ by Igor Vasiljevic, et al. ∙ 0 ∙ share

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Kalman-based Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning

Modern proximal and stochastic gradient descent (SGD) methods are believ...
12/03/2015 ∙ by Vivak Patel, et al. ∙ 0 ∙ share

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AUC-maximized Deep Convolutional Neural Fields for Sequence Labeling

Deep Convolutional Neural Networks (DCNN) has shown excellent performanc...
11/17/2015 ∙ by Sheng Wang, et al. ∙ 0 ∙ share

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Mixtures of Sparse Autoregressive Networks

We consider high-dimensional distribution estimation through autoregress...
11/15/2015 ∙ by Marc Goessling, et al. ∙ 0 ∙ share

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Distributed Multitask Learning

We consider the problem of distributed multi-task learning, where each m...
10/02/2015 ∙ by Jialei Wang, et al. ∙ 0 ∙ share

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A Statistical Theory of Deep Learning via Proximal Splitting

In this paper we develop a statistical theory and an implementation of d...
09/20/2015 ∙ by Nicholas G. Polson, et al. ∙ 0 ∙ share

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