b'Dustin Tran'

research

∙ 03/07/2023

Larger language models do in-context learning differently

We study how in-context learning (ICL) in language models is affected by...

0 Jerry Wei, et al. ∙

research

∙ 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...

0 James Urquhart Allingham, et al. ∙

research

∙ 11/23/2022

Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks

Bayesian deep learning seeks to equip deep neural networks with the abil...

0 Neil Band, et al. ∙

research

∙ 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...

54 Jeremiah Zhe Liu, et al. ∙

research

∙ 10/07/2021

Sparse MoEs meet Efficient Ensembles

Machine learning models based on the aggregated outputs of submodels, ei...

3 James Urquhart Allingham, et al. ∙

research

∙ 10/06/2021

Deep Classifiers with Label Noise Modeling and Distance Awareness

Uncertainty estimation in deep learning has recently emerged as a crucia...

0 Vincent Fortuin, et al. ∙

research

∙ 07/30/2021

Soft Calibration Objectives for Neural Networks

Optimal decision making requires that classifiers produce uncertainty es...

0 Archit Karandikar, et al. ∙

research

∙ 06/15/2021

Revisiting the Calibration of Modern Neural Networks

Accurate estimation of predictive uncertainty (model calibration) is ess...

18 Matthias Minderer, et al. ∙

research

∙ 06/07/2021

Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning

High-quality estimates of uncertainty and robustness are crucial for num...

0 Zachary Nado, et al. ∙

research

∙ 03/14/2021

RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems

The development of recommender systems that optimize multi-turn interact...

0 Martin Mladenov, et al. ∙

research

∙ 10/19/2020

Combining Ensembles and Data Augmentation can Harm your Calibration

Ensemble methods which average over multiple neural network predictions ...

0 Yeming Wen, et al. ∙

research

∙ 10/13/2020

Training independent subnetworks for robust prediction

Recent approaches to efficiently ensemble neural networks have shown tha...

0 Marton Havasi, et al. ∙

research

∙ 06/24/2020

Hyperparameter Ensembles for Robustness and Uncertainty Quantification

Ensembles over neural network weights trained from different random init...

13 Florian Wenzel, et al. ∙

research

∙ 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...

20 Jeremiah Zhe Liu, et al. ∙

research

∙ 05/14/2020

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

Bayesian neural networks (BNNs) demonstrate promising success in improvi...

11 Michael W. Dusenberry, et al. ∙

research

∙ 02/17/2020

On the Discrepancy between Density Estimation and Sequence Generation

Many sequence-to-sequence generation tasks, including machine translatio...

8 Jason Lee, et al. ∙

research

∙ 02/17/2020

BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning

Ensembles, where multiple neural networks are trained individually and t...

13 Yeming Wen, et al. ∙

research

∙ 06/10/2019

Analyzing the Role of Model Uncertainty for Electronic Health Records

In medicine, both ethical and monetary costs of incorrect predictions ca...

3 Michael W. Dusenberry, et al. ∙

research

∙ 05/24/2019

Discrete Flows: Invertible Generative Models of Discrete Data

While normalizing flows have led to significant advances in modeling hig...

6 Dustin Tran, et al. ∙

research

∙ 04/02/2019

Measuring Calibration in Deep Learning

The reliability of a machine learning model's confidence in its predicti...

10 Jeremy Nixon, et al. ∙

research

∙ 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...

0 Matthew Hoffman, et al. ∙

research

∙ 12/10/2018

Bayesian Layers: A Module for Neural Network Uncertainty

We describe Bayesian Layers, a module designed for fast experimentation ...

14 Dustin Tran, et al. ∙

research

∙ 11/29/2018

Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language

Deriving conditional and marginal distributions using conjugacy relation...

6 Matthew D. Hoffman, et al. ∙

research

∙ 11/05/2018

Simple, Distributed, and Accelerated Probabilistic Programming

We describe a simple, low-level approach for embedding probabilistic pro...

6 Dustin Tran, et al. ∙

research

∙ 11/05/2018

Mesh-TensorFlow: Deep Learning for Supercomputers

Batch-splitting (data-parallelism) is the dominant distributed Deep Neur...

8 Noam Shazeer, et al. ∙

research

∙ 07/24/2018

Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors

Obtaining reliable uncertainty estimates of neural network predictions i...

8 Danijar Hafner, et al. ∙

research

∙ 03/12/2018

Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches

Stochastic neural net weights are used in a variety of contexts, includi...

0 Yeming Wen, et al. ∙

research

∙ 11/28/2017

TensorFlow Distributions

The TensorFlow Distributions library implements a vision of probability ...

0 Joshua V. Dillon, et al. ∙

research

∙ 10/30/2017

Implicit Causal Models for Genome-wide Association Studies

Progress in probabilistic generative models has accelerated, developing ...

0 Dustin Tran, et al. ∙

research

∙ 02/28/2017

Hierarchical Implicit Models and Likelihood-Free Variational Inference

Implicit probabilistic models are a flexible class of models defined by ...

0 Dustin Tran, et al. ∙

research

∙ 01/13/2017

Deep Probabilistic Programming

We propose Edward, a Turing-complete probabilistic programming language....

0 Dustin Tran, et al. ∙

research

∙ 11/01/2016

Variational Inference via χ-Upper Bound Minimization

Variational inference (VI) is widely used as an efficient alternative to...

0 Adji B. Dieng, et al. ∙

research

∙ 10/31/2016

Edward: A library for probabilistic modeling, inference, and criticism

Probabilistic modeling is a powerful approach for analyzing empirical in...

0 Dustin Tran, et al. ∙

research

∙ 10/27/2016

Operator Variational Inference

Variational inference is an umbrella term for algorithms which cast Baye...

0 Rajesh Ranganath, et al. ∙

research

∙ 03/02/2016

Automatic Differentiation Variational Inference

Probabilistic modeling is iterative. A scientist posits a simple model, ...

0 Alp Kucukelbir, et al. ∙

research

∙ 11/20/2015

The Variational Gaussian Process

Variational inference is a powerful tool for approximate inference, and ...

0 Dustin Tran, et al. ∙

research

∙ 11/07/2015

Hierarchical Variational Models

Black box variational inference allows researchers to easily prototype a...

0 Rajesh Ranganath, et al. ∙

research

∙ 09/22/2015

Stochastic gradient descent methods for estimation with large data sets

We develop methods for parameter estimation in settings with large-scale...

0 Dustin Tran, et al. ∙

research

∙ 06/10/2015

Copula variational inference

We develop a general variational inference method that preserves depende...

0 Dustin Tran, et al. ∙

research

∙ 05/10/2015

Towards stability and optimality in stochastic gradient descent

Iterative procedures for parameter estimation based on stochastic gradie...

0 Panos Toulis, et al. ∙

research

∙ 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...

0 Andrew Gelman, et al. ∙

research

∙ 11/27/2014

Convex Techniques for Model Selection

We develop a robust convex algorithm to select the regularization parame...

0 Dustin Tran, et al. ∙

Dustin Tran

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