David Scott Krueger

Charlotte Stix

Peter Mark Henderson

Logan Graham

Carina E. A. Prunkl

Bianca Martin

Elizabeth Seger

Noa Zilberman

Sean O hEigeartaigh

Frens Kroeger

Girish Sastry

R. Kagan

Adrian Weller

Brian Shek-kam Tse

Elizabeth Barnes

Allan Dafoe

Paul D. Scharre

Ariel Herbert-Voss

Martijn Rasser

Shagun Sodhani

Carrick Flynn

Thomas Krendl Gilbert

Lisa Dyer

Saif M. Khan

Yoshua Bengio

Markus Anderljung

2020-04-15

ArXiv (prépublication)

Neural Autoregressive Flows

Chin-Wei Huang

Alexandre Lacoste

Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via M… (voir plus)asked Autoregressive Flows (MAF), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster than real-time, via Inverse Autoregressive Flows (IAF). We unify and generalize these approaches, replacing the (conditionally) affine univariate transformations of MAF/IAF with a more general class of invertible univariate transformations expressed as monotonic neural networks. We demonstrate that the proposed neural autoregressive flows (NAF) are universal approximators for continuous probability distributions, and their greater expressivity allows them to better capture multimodal target distributions. Experimentally, NAF yields state-of-the-art performance on a suite of density estimation tasks and outperforms IAF in variational autoencoders trained on binarized MNIST.

2018-07-03

Proceedings of the 35th International Conference on Machine Learning (publié)

proceedings.mlr.press

Bayesian Hypernetworks

Chin-Wei Huang

Riashat Islam

Ryan Turner

Alexandre Lacoste

2017-10-13

ArXiv (prépublication)

Bayesian Hypernetworks

Chin-Wei Huang

Riashat Islam

Ryan Turner

Alexandre Lacoste

We propose Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork, h, is a neura… (voir plus)l network which learns to transform a simple noise distribution, p(e) = N(0,I), to a distribution q(t) := q(h(e)) over the parameters t of another neural network (the ``primary network). We train q with variational inference, using an invertible h to enable efficient estimation of the variational lower bound on the posterior p(t | D) via sampling. In contrast to most methods for Bayesian deep learning, Bayesian hypernets can represent a complex multimodal approximate posterior with correlations between parameters, while enabling cheap iid sampling of q(t). In practice, Bayesian hypernets provide a better defense against adversarial examples than dropout, and also exhibit competitive performance on a suite of tasks which evaluate model uncertainty, including regularization, active learning, and anomaly detection.

2017-10-13

ArXiv (prépublication)

A Closer Look at Memorization in Deep Networks

Devansh Arpit

Stanisław Jastrzębski

Nicolas Ballas

Maxinder S. Kanwal

Asja Fischer

We examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While dee… (voir plus)p networks are capable of memorizing noise data, our results suggest that they tend to prioritize learning simple patterns first. In our experiments, we expose qualitative differences in gradient-based optimization of deep neural networks (DNNs) on noise vs. real data. We also demonstrate that for appropriately tuned explicit regularization (e.g., dropout) we can degrade DNN training performance on noise datasets without compromising generalization on real data. Our analysis suggests that the notions of effective capacity which are dataset independent are unlikely to explain the generalization performance of deep networks when trained with gradient based methods because training data itself plays an important role in determining the degree of memorization.

2017-07-17

Proceedings of the 34th International Conference on Machine Learning (publié)

proceedings.mlr.press

Deep Nets Don't Learn via Memorization

Nicolas Ballas

Stanisław Jastrzębski

Devansh Arpit

Maxinder S. Kanwal

Tegan Maharaj

Emmanuel Bengio

Asja Fischer

We use empirical methods to argue that deep neural networks (DNNs) do not achieve their performance by memorizing training data in spite of … (voir plus)overlyexpressive model architectures. Instead, they learn a simple available hypothesis that fits the finite data samples. In support of this view, we establish that there are qualitative differences when learning noise vs. natural datasets, showing: (1) more capacity is needed to fit noise, (2) time to convergence is longer for random labels, but shorter for random inputs, and (3) that DNNs trained on real data examples learn simpler functions than when trained with noise data, as measured by the sharpness of the loss function at convergence. Finally, we demonstrate that for appropriately tuned explicit regularization, e.g. dropout, we can degrade DNN training performance on noise datasets without compromising generalization on real data.

2017-02-17

International Conference on Learning Representations (publié)

dblp.uni-trier.de

Facilitating Multimodality in Normalizing Flows

Chin-Wei Huang