103 citations · 325 across the 14 of their papers we have counts for
10 papers · 1 filter
Iterative Importance Fine-tuning of Diffusion Models
Alexander Denker, Shreyas Padhy, Francisco Vargas +1
Diffusion models are an important tool for generative modelling, serving as effective priors in applications such as imaging and protein design. A key challenge in applying diffusi…
DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised -transform
Alexander Denker, Francisco Vargas, Shreyas Padhy +7
Generative modelling paradigms based on denoising diffusion processes have emerged as a leading candidate for conditional sampling in inverse problems. In many real-world applicati…
Stochastic Gradient Descent for Gaussian Processes Done Right
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán +5
As is well known, both sampling from the posterior and computing the mean of the posterior in Gaussian process regression reduces to solving a large linear system of equations. We…
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Jihao Andreas Lin, Javier Antorán, Shreyas Padhy +3
Gaussian processes are a powerful framework for quantifying uncertainty and for sequential decision-making but are limited by the requirement of solving linear systems. In general,…
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren +7
Accurate uncertainty quantification is a major challenge in deep learning, as neural networks can make overconfident errors and assign high confidence predictions to out-of-distrib…
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
Jie Ren, Stanislav Fort, Jeremiah Liu +3
Mahalanobis distance (MD) is a simple and popular post-processing method for detecting out-of-distribution (OOD) inputs in neural networks. We analyze its failure modes for near-OO…
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Zachary Nado, Neil Band, Mark Collier +23
High-quality estimates of uncertainty and robustness are crucial for numerous real-world applications, especially for deep learning which underlies many deployed ML systems. The ab…
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks
Shreyas Padhy, Zachary Nado, Jie Ren +3
Accurate estimation of predictive uncertainty in modern neural networks is critical to achieve well calibrated predictions and detect out-of-distribution (OOD) inputs. The most pro…
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift
Zachary Nado, Shreyas Padhy, D. Sculley +3
Covariate shift has been shown to sharply degrade both predictive accuracy and the calibration of uncertainty estimates for deep learning models. This is worrying, because covariat…
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy +3
Bayesian neural networks (BNN) and deep ensembles are principled approaches to estimate the predictive uncertainty of a deep learning model. However their practicality in real-time…