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07/02/2023
Tools for Verifying Neural Models' Training Data
It is important that consumers and regulators can verify the provenance ...
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12/28/2022
On Implicit Bias in Overparameterized Bilevel Optimization
Many problems in machine learning involve bilevel optimization (BLO), in...
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11/02/2021
Meta-Learning to Improve Pre-Training
Pre-training (PT) followed by fine-tuning (FT) is an effective method fo...
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04/12/2021
Getting to the Point. Index Sets and Parallelism-Preserving Autodiff for Pointful Array Programming
We present a novel programming language design that attempts to combine ...
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02/16/2021
Complex Momentum for Learning in Games
We generalize gradient descent with momentum for learning in differentia...
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02/12/2021
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations
We perform scalable approximate inference in a recently-proposed family ...
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02/08/2021
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
We propose a general and scalable approximate sampling strategy for prob...
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11/09/2020
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering
Standard first-order stochastic optimization algorithms base their updat...
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11/05/2020
Teaching with Commentaries
Effective training of deep neural networks can be challenging, and there...
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10/08/2020
No MCMC for me: Amortized sampling for fast and stable training of energy-based models
Energy-Based Models (EBMs) present a flexible and appealing way to repre...
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07/09/2020
A Study of Gradient Variance in Deep Learning
The impact of gradient noise on training deep models is widely acknowled...
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07/09/2020
Learning Differential Equations that are Easy to Solve
Differential equations parameterized by neural networks become expensive...
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04/01/2020
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Standard variational lower bounds used to train latent variable models p...
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03/05/2020
What went wrong and when? Instance-wise Feature Importance for Time-series Models
Multivariate time series models are poised to be used for decision suppo...
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02/13/2020
Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling
We present a new method for evaluating and training unnormalized density...
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01/05/2020
Scalable Gradients for Stochastic Differential Equations
The adjoint sensitivity method scalably computes gradients of solutions ...
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12/08/2019
Neural Networks with Cheap Differential Operators
Gradients of neural networks can be computed efficiently for any archite...
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12/06/2019
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
We propose to reinterpret a standard discriminative classifier of p(y|x)...
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11/06/2019
Optimizing Millions of Hyperparameters by Implicit Differentiation
We propose an algorithm for inexpensive gradient-based hyperparameter op...
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10/02/2019
Efficient Graph Generation with Graph Recurrent Attention Networks
We propose a new family of efficient and expressive deep generative mode...
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08/18/2019
Understanding Undesirable Word Embedding Associations
Word embeddings are often criticized for capturing undesirable word asso...
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07/08/2019
Latent ODEs for Irregularly-Sampled Time Series
Time series with non-uniform intervals occur in many applications, and a...
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06/06/2019
Residual Flows for Invertible Generative Modeling
Flow-based generative models parameterize probability distributions thro...
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03/07/2019
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions
Hyperparameter optimization can be formulated as a bilevel optimization ...
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11/02/2018
Invertible Residual Networks
Reversible deep networks provide useful theoretical guarantees and have ...
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10/11/2018
Towards Understanding Linear Word Analogies
A surprising property of word vectors is that vector algebra can often b...
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10/02/2018
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
A promising class of generative models maps points from a simple distrib...
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08/20/2018
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples
Many deep learning algorithms can be easily fooled with simple adversari...
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07/20/2018
Explaining Image Classifiers by Adaptive Dropout and Generative In-filling
Explanations of black-box classifiers often rely on saliency maps, which...
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07/05/2018
Scalable Recommender Systems through Recursive Evidence Chains
Recommender systems can be formulated as a matrix completion problem, pr...
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06/19/2018
Neural Ordinary Differential Equations
We introduce a new family of deep neural network models. Instead of spec...
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02/26/2018
Stochastic Hyperparameter Optimization through Hypernetworks
Machine learning models are often tuned by nesting optimization of model...
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02/14/2018
Isolating Sources of Disentanglement in Variational Autoencoders
We decompose the evidence lower bound to show the existence of a term me...
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01/10/2018
Inference Suboptimality in Variational Autoencoders
Amortized inference has led to efficient approximate inference for large...
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12/17/2017
Generating and designing DNA with deep generative models
We propose generative neural network methods to generate DNA sequences a...
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12/06/2017
Noisy Natural Gradient as Variational Inference
Combining the flexibility of deep learning with Bayesian uncertainty est...
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04/10/2017
Reinterpreting Importance-Weighted Autoencoders
The standard interpretation of importance-weighted autoencoders is that ...
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03/27/2017
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference
We propose a simple and general variant of the standard reparameterized ...
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08/22/2016
Neural networks for the prediction organic chemistry reactions
Reaction prediction remains one of the major challenges for organic chem...
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03/20/2016
Composing graphical models with neural networks for structured representations and fast inference
We propose a general modeling and inference framework that composes prob...
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09/30/2015
Convolutional Networks on Graphs for Learning Molecular Fingerprints
We introduce a convolutional neural network that operates directly on gr...
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04/06/2015
Early Stopping is Nonparametric Variational Inference
We show that unconverged stochastic gradient descent can be interpreted ...
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02/11/2015
Gradient-based Hyperparameter Optimization through Reversible Learning
Tuning hyperparameters of learning algorithms is hard because gradients ...
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08/09/2014
Warped Mixtures for Nonparametric Cluster Shapes
A mixture of Gaussians fit to a single curved or heavy-tailed cluster wi...
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08/09/2014
Optimally-Weighted Herding is Bayesian Quadrature
Herding and kernel herding are deterministic methods of choosing samples...
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02/24/2014
Avoiding pathologies in very deep networks
Choosing appropriate architectures and regularization strategies for dee...
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02/18/2014
Automatic Construction and Natural-Language Description of Nonparametric Regression Models
This paper presents the beginnings of an automatic statistician, focusin...
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02/20/2013
Structure Discovery in Nonparametric Regression through Compositional Kernel Search
Despite its importance, choosing the structural form of the kernel in no...
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12/19/2011
