Trustworthiness
We study whether a neural network optimizes to the same, linearly connected minimum under different samples of SGD noise (e.g., random …
In this work, we focus on the use of influence functions to identify relevant training examples that one might hope …
Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their …
Pruning is a well-established technique for removing unnecessary structure from neural networks after training to improve the …
We study whether a neural network optimizes to the same, linearly connected minimum under different samples of SGD noise (e.g., random …
In this work, we improve upon the stepwise analysis of noisy iterative learning algorithms initiated by Pensia, Jog, and Loh (2018) and …
We introduce a machine learning workflow to search for, identify, and meaningfully triage videos and images of munitions, weapons, and …
Recent works have shown that stochastic gradient descent (SGD) achieves the fast convergence rates of full-batch gradient descent for …
In this work, we improve upon the stepwise analysis of noisy iterative learning algorithms initiated by Pensia, Jog, and Loh (2018) and …
Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their …