
Molecular Latent Space Simulators
Small integration time steps limit molecular dynamics (MD) simulations t...
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QuantumNAS: NoiseAdaptive Search for Robust Quantum Circuits
Quantum noise is the key challenge in Noisy IntermediateScale Quantum (...
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Deformable Style Transfer
Both geometry and texture are fundamental aspects of visual style. Exist...
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VAEBM: A Symbiosis between Variational Autoencoders and Energybased Models
Energybased models (EBMs) have recently been successful in representing...
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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...
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Human Evaluation of Interpretability: The Case of AIGenerated Music Knowledge
Interpretability of machine learning models has gained more and more att...
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ARDA: Automatic Relational Data Augmentation for Machine Learning
Automatic machine learning () is a family of techniques to automate the ...
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Style Transfer by Relaxed Optimal Transport and SelfSimilarity
Style transfer algorithms strive to render the content of one image usin...
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Too many cooks: Coordinating multiagent collaboration through inverse planning
Collaboration requires agents to coordinate their behavior on the fly, s...
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Nonlinear Discovery of Slow Molecular Modes using Hierarchical Dynamics Encoders
The success of enhanced sampling molecular simulations that accelerate a...
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Fingerspelling recognition in the wild with iterative visual attention
Sign language recognition is a challenging gesture sequence recognition ...
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Pixel Consensus Voting for Panoptic Segmentation
The core of our approach, Pixel Consensus Voting, is a framework for ins...
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Growing Efficient Deep Networks by Structured Continuous Sparsification
We develop an approach to training deep networks while dynamically adjus...
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Deep Learning for Automated Classification and Characterization of Amorphous Materials
It is difficult to quantify structureproperty relationships and to iden...
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Domainindependent Dominance of Adaptive Methods
From a simplified analysis of adaptive methods, we derive AvaGrad, a new...
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Is Local SGD Better than Minibatch SGD?
We study local SGD (also known as parallel SGD and federated averaging),...
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Scalable Data Augmentation for Deep Learning
Scalable Data Augmentation (SDA) provides a framework for training deep ...
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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...
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Generating Diverse Story Continuations with Controllable Semantics
We propose a simple and effective modeling framework for controlled gene...
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Are Pretrained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction
With the recent success and popularity of pretrained language models (L...
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Phase Transitions in Approximate Ranking
We study the problem of approximate ranking from observations of pairwis...
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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...
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Stochastic Strictly Contractive PeacemanRachford Splitting Method
In this paper, we propose a couple of new Stochastic Strictly Contractiv...
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RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning
Anonymized electronic medical records are an increasingly popular source...
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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...
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The Impact of Local Geometry and Batch Size on the Convergence and Divergence of Stochastic Gradient Descent
Stochastic smallbatch (SB) methods, such as minibatch Stochastic Gradi...
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Sparse Regularization in Marketing and Economics
Sparse alphanorm regularization has many datarich applications in mark...
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Evaluation of a Treebased Pipeline Optimization Tool for Automating Data Science
As the field of data science continues to grow, there will be an everin...
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Bayesian l_0 Regularized Least Squares
Bayesian l_0regularized least squares provides a variable selection tec...
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Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering
We propose the Gaussian attention model for contentbased neural memory ...
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Interpreting Neural Networks to Improve Politeness Comprehension
We present an interpretable neural network approach to predicting and un...
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LogitBoost autoregressive networks
Multivariate binary distributions can be decomposed into products of uni...
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A unified treatment of multiple testing with prior knowledge using the pfilter
A significant literature studies ways of employing prior knowledge to im...
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Stochastic Canonical Correlation Analysis
We tightly analyze the sample complexity of CCA, provide a learning algo...
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Dynamic Partition Models
We present a new approach for learning compact and intuitive distributed...
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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...
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Colorization as a Proxy Task for Visual Understanding
We investigate and improve selfsupervision as a dropin replacement for...
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A Geometrical Approach to Topic Model Estimation
In the probabilistic topic models, the quantity of interesta lowrank...
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Ensemble preconditioning for Markov chain Monte Carlo simulation
We describe parallel Markov chain Monte Carlo methods that propagate a c...
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Machine Learning for Antimicrobial Resistance
Biological datasets amenable to applied machine learning are more availa...
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Jointly Learning Multiple Measures of Similarities from Triplet Comparisons
Similarity between objects is multifaceted and it can be easier for hum...
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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...
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Edgeexchangeable graphs and sparsity
A known failing of many popular random graph models is that the AldousH...
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Identifiability of an Xrank decomposition of polynomial maps
In this paper, we study a polynomial decomposition model that arises in ...
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Examining the Impact of Blur on Recognition by Convolutional Networks
Stateoftheart algorithms for many semantic visual tasks are based on ...
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Kalmanbased Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning
Modern proximal and stochastic gradient descent (SGD) methods are believ...
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AUCmaximized Deep Convolutional Neural Fields for Sequence Labeling
Deep Convolutional Neural Networks (DCNN) has shown excellent performanc...
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Mixtures of Sparse Autoregressive Networks
We consider highdimensional distribution estimation through autoregress...
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Distributed Multitask Learning
We consider the problem of distributed multitask learning, where each m...
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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...
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