Mihaela Rosca | DeepAI

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∙ 07/11/2023

Implicit regularisation in stochastic gradient descent: from single-objective to two-player games

Recent years have seen many insights on deep learning optimisation being...

0 Mihaela Rosca, et al. ∙

∙ 07/09/2023

Investigating the Edge of Stability Phenomenon in Reinforcement Learning

Recent progress has been made in understanding optimisation dynamics in ...

0 Rares Iordan, et al. ∙

∙ 09/27/2022

Why neural networks find simple solutions: the many regularizers of geometric complexity

In many contexts, simpler models are preferable to more complex models a...

0 Benoit Dherin, et al. ∙

∙ 05/28/2021

Discretization Drift in Two-Player Games

Gradient-based methods for two-player games produce rich dynamics that c...

0 Mihaela Rosca, et al. ∙

∙ 05/11/2021

Spectral Normalisation for Deep Reinforcement Learning: an Optimisation Perspective

Most of the recent deep reinforcement learning advances take an RL-centr...

7 Florin Gogianu, et al. ∙

∙ 12/14/2020

A case for new neural network smoothness constraints

How sensitive should machine learning models be to input changes? We tac...

0 Mihaela Rosca, et al. ∙

∙ 06/25/2019

Monte Carlo Gradient Estimation in Machine Learning

This paper is a broad and accessible survey of the methods we have at ou...

10 Shakir Mohamed, et al. ∙

∙ 05/23/2019

Training language GANs from Scratch

Generative Adversarial Networks (GANs) enjoy great success at image gene...

14 Cyprien de Masson d'Autume, et al. ∙

∙ 05/16/2019

Deep Compressed Sensing

Compressed sensing (CS) provides an elegant framework for recovering spa...

0 Yan Wu, et al. ∙

∙ 06/28/2018

Learning Implicit Generative Models with the Method of Learned Moments

We propose a method of moments (MoM) algorithm for training large-scale ...

2 Suman Ravuri, et al. ∙

∙ 02/19/2018

Distribution Matching in Variational Inference

The difficulties in matching the latent posterior to the prior, balancin...

0 Mihaela Rosca, et al. ∙

∙ 10/23/2017

Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step

Generative adversarial networks (GANs) are a family of generative models...

0 William Fedus, et al. ∙

∙ 06/15/2017

Variational Approaches for Auto-Encoding Generative Adversarial Networks

Auto-encoding generative adversarial networks (GANs) combine the standar...

0 Mihaela Rosca, et al. ∙

∙ 10/29/2016

Sequence-to-sequence neural network models for transliteration

Transliteration is a key component of machine translation systems and so...

0 Mihaela Rosca, et al. ∙

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