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Linear Mode Connectivity and the Lottery Ticket Hypothesis
We study whether a neural network optimizes to the same, linearly connected minimum under different samples of SGD noise (e.g., random …
RelatIF: Identifying Explanatory Training Examples via Relative Influence
In this work, we focus on the use of influence functions to identify relevant training examples that one might hope …
Stochastic Neural Network with Kronecker Flow
Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their …
Stabilizing the Lottery Ticket Hypothesis
Pruning is a well-established technique for removing unnecessary structure from neural networks after training to improve the …
Linear Mode Connectivity and the Lottery Ticket Hypothesis
We study whether a neural network optimizes to the same, linearly connected minimum under different samples of SGD noise (e.g., random …
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
In this work, we improve upon the stepwise analysis of noisy iterative learning algorithms initiated by Pensia, Jog, and Loh (2018) and …
Objects of violence: synthetic data for practical ML in human rights investigations
We introduce a machine learning workflow to search for, identify, and meaningfully triage videos and images of munitions, weapons, and …
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
Recent works have shown that stochastic gradient descent (SGD) achieves the fast convergence rates of full-batch gradient descent for …
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
In this work, we improve upon the stepwise analysis of noisy iterative learning algorithms initiated by Pensia, Jog, and Loh (2018) and …
Stochastic Neural Network with Kronecker Flow
Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their …