
On the Difficulty of WarmStarting Neural Network Training
In many realworld deployments of machine learning systems, data arrive ...
10/18/2019 ∙ by Jordan T. Ash, et al. ∙ 18 ∙ shareread it

A Theoretical Connection Between Statistical Physics and Reinforcement Learning
Sequential decision making in the presence of uncertainty and stochastic...
06/24/2019 ∙ by Jad Rahme, et al. ∙ 6 ∙ shareread it

SpArSe: Sparse Architecture Search for CNNs on ResourceConstrained Microcontrollers
The vast majority of processors in the world are actually microcontrolle...
05/28/2019 ∙ by Igor Fedorov, et al. ∙ 5 ∙ shareread it

Predicting ElectronIonization Mass Spectrometry using Neural Networks
When confronted with a substance of unknown identity, researchers often ...
11/21/2018 ∙ by Jennifer N. Wei, et al. ∙ 4 ∙ shareread it

Efficient Optimization of Loops and Limits with Randomized Telescoping Sums
We consider optimization problems in which the objective requires an inn...
05/16/2019 ∙ by Alex Beatson, et al. ∙ 4 ∙ shareread it

Discrete Object Generation with Reversible Inductive Construction
The success of generative modeling in continuous domains has led to a su...
07/18/2019 ∙ by Ari Seff, et al. ∙ 3 ∙ shareread it

Practical Bayesian Optimization of Machine Learning Algorithms
Machine learning algorithms frequently require careful tuning of model h...
06/13/2012 ∙ by Jasper Snoek, et al. ∙ 0 ∙ shareread it

Reducing Reparameterization Gradient Variance
Optimization with noisy gradients has become ubiquitous in statistics an...
05/22/2017 ∙ by Andrew C. Miller, et al. ∙ 0 ∙ shareread it

Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
Professionalgrade software applications are powerful but complicatedex...
04/17/2017 ∙ by Ardavan Saeedi, et al. ∙ 0 ∙ shareread it

Variational Boosting: Iteratively Refining Posterior Approximations
We propose a blackbox variational inference method to approximate intra...
11/20/2016 ∙ by Andrew C. Miller, et al. ∙ 0 ∙ shareread it

Recurrent switching linear dynamical systems
Many natural systems, such as neurons firing in the brain or basketball ...
10/26/2016 ∙ by Scott W. Linderman, et al. ∙ 0 ∙ shareread it

Bayesian latent structure discovery from multineuron recordings
Neural circuits contain heterogeneous groups of neurons that differ in t...
10/26/2016 ∙ by Scott W. Linderman, et al. ∙ 0 ∙ shareread it

Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation
A good clustering can help a data analyst to explore and understand a da...
06/19/2016 ∙ by Akash Srivastava, et al. ∙ 0 ∙ shareread it

Composing graphical models with neural networks for structured representations and fast inference
We propose a general modeling and inference framework that composes prob...
03/20/2016 ∙ by Matthew J. Johnson, et al. ∙ 0 ∙ shareread it

Patterns of Scalable Bayesian Inference
Datasets are growing not just in size but in complexity, creating a dema...
02/16/2016 ∙ by Elaine Angelino, et al. ∙ 0 ∙ shareread it

A General Framework for Constrained Bayesian Optimization using Informationbased Search
We present an informationtheoretic framework for solving global blackb...
11/30/2015 ∙ by José Miguel HernándezLobato, et al. ∙ 0 ∙ shareread it

Predictive Entropy Search for Multiobjective Bayesian Optimization
We present PESMO, a Bayesian method for identifying the Pareto set of mu...
11/17/2015 ∙ by Daniel HernándezLobato, et al. ∙ 0 ∙ shareread it

Sandwiching the marginal likelihood using bidirectional Monte Carlo
Computing the marginal likelihood (ML) of a model requires marginalizing...
11/08/2015 ∙ by Roger B. Grosse, et al. ∙ 0 ∙ shareread it

Convolutional Networks on Graphs for Learning Molecular Fingerprints
We introduce a convolutional neural network that operates directly on gr...
09/30/2015 ∙ by David Duvenaud, et al. ∙ 0 ∙ shareread it

Dependent Multinomial Models Made Easy: Stick Breaking with the PólyaGamma Augmentation
Many practical modeling problems involve discrete data that are best rep...
06/18/2015 ∙ by Scott W. Linderman, et al. ∙ 0 ∙ shareread it

Spectral Representations for Convolutional Neural Networks
Discrete Fourier transforms provide a significant speedup in the computa...
06/11/2015 ∙ by Oren Rippel, et al. ∙ 0 ∙ shareread it

Early Stopping is Nonparametric Variational Inference
We show that unconverged stochastic gradient descent can be interpreted ...
04/06/2015 ∙ by Dougal Maclaurin, et al. ∙ 0 ∙ shareread it

Scalable Bayesian Optimization Using Deep Neural Networks
Bayesian optimization is an effective methodology for the global optimiz...
02/19/2015 ∙ by Jasper Snoek, et al. ∙ 0 ∙ shareread it

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
Large multilayer neural networks trained with backpropagation have recen...
02/18/2015 ∙ by José Miguel HernándezLobato, et al. ∙ 0 ∙ shareread it

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
Unknown constraints arise in many types of expensive blackbox optimizat...
02/18/2015 ∙ by José Miguel HernándezLobato, et al. ∙ 0 ∙ shareread it

Gradientbased Hyperparameter Optimization through Reversible Learning
Tuning hyperparameters of learning algorithms is hard because gradients ...
02/11/2015 ∙ by Dougal Maclaurin, et al. ∙ 0 ∙ shareread it

A framework for studying synaptic plasticity with neural spike train data
Learning and memory in the brain are implemented by complex, timevaryin...
11/14/2014 ∙ by Scott W. Linderman, et al. ∙ 0 ∙ shareread it

Accelerating MCMC via Parallel Predictive Prefetching
We present a general framework for accelerating a large class of widely ...
03/28/2014 ∙ by Elaine Angelino, et al. ∙ 0 ∙ shareread it

Firefly Monte Carlo: Exact MCMC with Subsets of Data
Markov chain Monte Carlo (MCMC) is a popular and successful generalpurp...
03/22/2014 ∙ by Dougal Maclaurin, et al. ∙ 0 ∙ shareread it

Bayesian Optimization with Unknown Constraints
Recent work on Bayesian optimization has shown its effectiveness in glob...
03/22/2014 ∙ by Michael A. Gelbart, et al. ∙ 0 ∙ shareread it

Avoiding pathologies in very deep networks
Choosing appropriate architectures and regularization strategies for dee...
02/24/2014 ∙ by David Duvenaud, et al. ∙ 0 ∙ shareread it

Learning the Parameters of Determinantal Point Process Kernels
Determinantal point processes (DPPs) are wellsuited for modeling repuls...
02/20/2014 ∙ by Raja Hafiz Affandi, et al. ∙ 0 ∙ shareread it

Input Warping for Bayesian Optimization of Nonstationary Functions
Bayesian optimization has proven to be a highly effective methodology fo...
02/05/2014 ∙ by Jasper Snoek, et al. ∙ 0 ∙ shareread it

Learning Ordered Representations with Nested Dropout
In this paper, we study ordered representations of data in which differe...
02/05/2014 ∙ by Oren Rippel, et al. ∙ 0 ∙ shareread it

Discovering Latent Network Structure in Point Process Data
Networks play a central role in modern data analysis, enabling us to rea...
02/04/2014 ∙ by Scott W. Linderman, et al. ∙ 0 ∙ shareread it

ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures
The Dirichlet process (DP) is a fundamental mathematical tool for Bayesi...
04/08/2013 ∙ by Dan Lovell, et al. ∙ 0 ∙ shareread it

Parallel MCMC with Generalized Elliptical Slice Sampling
Probabilistic models are conceptually powerful tools for finding structu...
10/28/2012 ∙ by Robert Nishihara, et al. ∙ 0 ∙ shareread it

Training Restricted Boltzmann Machines on Word Observations
The restricted Boltzmann machine (RBM) is a flexible tool for modeling c...
02/25/2012 ∙ by George E. Dahl, et al. ∙ 0 ∙ shareread it

PASSGLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Generalized linear models (GLMs)  such as logistic regression, Poisson...
09/26/2017 ∙ by Jonathan H. Huggins, et al. ∙ 0 ∙ shareread it

Compressibility and Generalization in LargeScale Deep Learning
Modern neural networks are highly overparameterized, with capacity to su...
04/16/2018 ∙ by Wenda Zhou, et al. ∙ 0 ∙ shareread it

Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical c...
02/28/2018 ∙ by Jeffrey Regier, et al. ∙ 0 ∙ shareread it

Motivating the Rules of the Game for Adversarial Example Research
Advances in machine learning have led to broad deployment of systems wit...
07/18/2018 ∙ by Justin Gilmer, et al. ∙ 0 ∙ shareread it

Estimating the Spectral Density of Large Implicit Matrices
Many important problems are characterized by the eigenvalues of a large ...
02/09/2018 ∙ by Ryan P. Adams, et al. ∙ 0 ∙ shareread it