353 citations · 362 across the 3 of their papers we have counts for
3 papers
Nested LSTMs
Joel Ruben Antony Moniz, David Krueger
We propose Nested LSTMs (NLSTM), a novel RNN architecture with multiple levels of memory. Nested LSTMs add depth to LSTMs via nesting as opposed to stacking. The value of a memory…
Deep Prior
Alexandre Lacoste, Thomas Boquet, Negar Rostamzadeh +3
The recent literature on deep learning offers new tools to learn a rich probability distribution over high dimensional data such as images or sounds. In this work we investigate th…
A Closer Look at Memorization in Deep Networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas +8
We examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While deep networks are capable of memorizing noi…