4.7k citations · 10.6k across the 33 of their papers we have counts for
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mixup: Beyond Empirical Risk Minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin +1
Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple le…
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
Mike Lewis, Denis Yarats, Yann N. Dauphin +2
Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and r…
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks
Levent Sagun, Utku Evci, V. Ugur Guney +2
We study the properties of common loss surfaces through their Hessian matrix. In particular, in the context of deep learning, we empirically show that the spectrum of the Hessian i…
Tackling Over-pruning in Variational Autoencoders
Serena Yeung, Anitha Kannan, Yann Dauphin +1
Variational autoencoders (VAE) are directed generative models that learn factorial latent variables. As noted by Burda et al. (2015), these models exhibit the problem of factor ove…
Parseval Networks: Improving Robustness to Adversarial Examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave +2
We introduce Parseval networks, a form of deep neural networks in which the Lipschitz constant of linear, convolutional and aggregation layers is constrained to be smaller than 1.…
Convolutional Sequence to Sequence Learning
Jonas Gehring, Michael Auli, David Grangier +2
The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture base…