1 code implementation • 10 Feb 2023 • Ryan Gillard, Stephen Jonany, Yingjie Miao, Michael Munn, Connal de Souza, Jonathan Dungay, Chen Liang, David R. So, Quoc V. Le, Esteban Real
In this paper, we show that large efficiency gains can be obtained by employing a fast unified functional hash, especially through the functional equivalence caching technique, which we also present.
no code implementations • 27 Sep 2022 • Benoit Dherin, Michael Munn, Mihaela Rosca, David G. T. Barrett
Using a combination of theoretical arguments and empirical results, we show that many common training heuristics such as parameter norm regularization, spectral norm regularization, flatness regularization, implicit gradient regularization, noise regularization and the choice of parameter initialization all act to control geometric complexity, providing a unifying framework in which to characterize the behavior of deep learning models.
no code implementations • 30 Nov 2021 • Benoit Dherin, Michael Munn, David G. T. Barrett
We argue that over-parameterized neural networks trained with stochastic gradient descent are subject to a Geometric Occam's Razor; that is, these networks are implicitly regularized by the geometric model complexity.
1 code implementation • NeurIPS 2020 • Tianlin Xu, Li K. Wenliang, Michael Munn, Beatrice Acciaio
We introduce COT-GAN, an adversarial algorithm to train implicit generative models optimized for producing sequential data.