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03/07/2023
Larger language models do in-context learning differently
We study how in-context learning (ICL) in language models is affected by...
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02/13/2023
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models
Contrastively trained text-image models have the remarkable ability to p...
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11/23/2022
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks
Bayesian deep learning seeks to equip deep neural networks with the abil...
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05/01/2022
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Accurate uncertainty quantification is a major challenge in deep learnin...
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10/07/2021
Sparse MoEs meet Efficient Ensembles
Machine learning models based on the aggregated outputs of submodels, ei...
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10/06/2021
Deep Classifiers with Label Noise Modeling and Distance Awareness
Uncertainty estimation in deep learning has recently emerged as a crucia...
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07/30/2021
Soft Calibration Objectives for Neural Networks
Optimal decision making requires that classifiers produce uncertainty es...
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06/15/2021
Revisiting the Calibration of Modern Neural Networks
Accurate estimation of predictive uncertainty (model calibration) is ess...
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06/07/2021
Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning
High-quality estimates of uncertainty and robustness are crucial for num...
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03/14/2021
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
The development of recommender systems that optimize multi-turn interact...
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10/19/2020
Combining Ensembles and Data Augmentation can Harm your Calibration
Ensemble methods which average over multiple neural network predictions ...
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10/13/2020
Training independent subnetworks for robust prediction
Recent approaches to efficiently ensemble neural networks have shown tha...
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06/24/2020
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Ensembles over neural network weights trained from different random init...
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06/17/2020
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Bayesian neural networks (BNN) and deep ensembles are principled approac...
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05/14/2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Bayesian neural networks (BNNs) demonstrate promising success in improvi...
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02/17/2020
On the Discrepancy between Density Estimation and Sequence Generation
Many sequence-to-sequence generation tasks, including machine translatio...
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02/17/2020
BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning
Ensembles, where multiple neural networks are trained individually and t...
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06/10/2019
Analyzing the Role of Model Uncertainty for Electronic Health Records
In medicine, both ethical and monetary costs of incorrect predictions ca...
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05/24/2019
Discrete Flows: Invertible Generative Models of Discrete Data
While normalizing flows have led to significant advances in modeling hig...
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04/02/2019
Measuring Calibration in Deep Learning
The reliability of a machine learning model's confidence in its predicti...
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03/09/2019
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Hamiltonian Monte Carlo is a powerful algorithm for sampling from diffic...
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12/10/2018
Bayesian Layers: A Module for Neural Network Uncertainty
We describe Bayesian Layers, a module designed for fast experimentation ...
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11/29/2018
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Deriving conditional and marginal distributions using conjugacy relation...
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11/05/2018
Simple, Distributed, and Accelerated Probabilistic Programming
We describe a simple, low-level approach for embedding probabilistic pro...
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11/05/2018
Mesh-TensorFlow: Deep Learning for Supercomputers
Batch-splitting (data-parallelism) is the dominant distributed Deep Neur...
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07/24/2018
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
Obtaining reliable uncertainty estimates of neural network predictions i...
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03/12/2018
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Stochastic neural net weights are used in a variety of contexts, includi...
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11/28/2017
TensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability ...
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10/30/2017
Implicit Causal Models for Genome-wide Association Studies
Progress in probabilistic generative models has accelerated, developing ...
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02/28/2017
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Implicit probabilistic models are a flexible class of models defined by ...
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01/13/2017
Deep Probabilistic Programming
We propose Edward, a Turing-complete probabilistic programming language....
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11/01/2016
Variational Inference via χ-Upper Bound Minimization
Variational inference (VI) is widely used as an efficient alternative to...
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10/31/2016
Edward: A library for probabilistic modeling, inference, and criticism
Probabilistic modeling is a powerful approach for analyzing empirical in...
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10/27/2016
Operator Variational Inference
Variational inference is an umbrella term for algorithms which cast Baye...
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03/02/2016
Automatic Differentiation Variational Inference
Probabilistic modeling is iterative. A scientist posits a simple model, ...
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11/20/2015
The Variational Gaussian Process
Variational inference is a powerful tool for approximate inference, and ...
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11/07/2015
Hierarchical Variational Models
Black box variational inference allows researchers to easily prototype a...
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09/22/2015
Stochastic gradient descent methods for estimation with large data sets
We develop methods for parameter estimation in settings with large-scale...
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06/10/2015
Copula variational inference
We develop a general variational inference method that preserves depende...
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05/10/2015
Towards stability and optimality in stochastic gradient descent
Iterative procedures for parameter estimation based on stochastic gradie...
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12/16/2014
Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
A common approach for Bayesian computation with big data is to partition...
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11/27/2014