180 citations · 190 across the 2 of their papers we have counts for
5 papers
TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning
Riyadh Baghdadi, Abdelkader Nadir Debbagh, Kamel Abdous +5
In this paper, we demonstrate a compiler that can optimize sparse and recurrent neural networks, both of which are currently outside of the scope of existing neural network compile…
Comparing Rewinding and Fine-tuning in Neural Network Pruning
Alex Renda, Jonathan Frankle, Michael Carbin
Many neural network pruning algorithms proceed in three steps: train the network to completion, remove unwanted structure to compress the network, and retrain the remaining structu…
Linear Mode Connectivity and the Lottery Ticket Hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy +1
We study whether a neural network optimizes to the same, linearly connected minimum under different samples of SGD noise (e.g., random data order and augmentation). We find that st…
Reactive Probabilistic Programming
Guillaume Baudart, Louis Mandel, Eric Atkinson +3
Synchronous modeling is at the heart of programming languages like Lustre, Esterel, or Scade used routinely for implementing safety critical control software, e.g., fly-by-wire and…
Stabilizing the Lottery Ticket Hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy +1
Pruning is a well-established technique for removing unnecessary structure from neural networks after training to improve the performance of inference. Several recent results have…