1.7k citations · 3.2k across the 31 of their papers we have counts for
6 papers · 1 filter
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning
Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Geoff Pleiss +1
Modern deep learning is primarily an experimental science, in which empirical advances occasionally come at the expense of probabilistic rigor. Here we focus on one such example; n…
Harnessing Interpretable and Unsupervised Machine Learning to Address Big Data from Modern X-ray Diffraction
Jordan Venderley, Michael Matty, Krishnanand Mallayya +14
The information content of crystalline materials becomes astronomical when collective electronic behavior and their fluctuations are taken into account. In the past decade, improve…
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Geoff Pleiss, Martin Jankowiak, David Eriksson +2
Matrix square roots and their inverses arise frequently in machine learning, e.g., when sampling from high-dimensional Gaussians or whitening a…
Deep Sigma Point Processes
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
We introduce Deep Sigma Point Processes, a class of parametric models inspired by the compositional structure of Deep Gaussian Processes (DGPs). Deep Sigma Point Processes (DSPPs)…
Convolutional Networks with Dense Connectivity
Gao Huang, Zhuang Liu, Geoff Pleiss +2
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the…
Identifying Mislabeled Data using the Area Under the Margin Ranking
Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg +1
Not all data in a typical training set help with generalization; some samples can be overly ambiguous or outrightly mislabeled. This paper introduces a new method to identify such…