412 citations · 919 across the 20 of their papers we have counts for
5 papers · 1 filter
BrainBERT: Self-supervised representation learning for intracranial recordings
Christopher Wang, Vighnesh Subramaniam, Adam Uri Yaari +4
We create a reusable Transformer, BrainBERT, for intracranial recordings bringing modern representation learning approaches to neuroscience. Much like in NLP and speech recognition…
Frivolous Units: Wider Networks Are Not Really That Wide
Stephen Casper, Xavier Boix, Vanessa D'Amario +4
A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their accuracy does not degrade when the network's width is increased. Recent evidence suggests…
On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations
Nicholas Cheney, Martin Schrimpf, Gabriel Kreiman
Deep convolutional neural networks are generally regarded as robust function approximators. So far, this intuition is based on perturbations to external stimuli such as the images…
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
William Lotter, Gabriel Kreiman, David Cox
While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning - leveraging unlabeled examples to lea…
Unsupervised Learning of Visual Structure using Predictive Generative Networks
William Lotter, Gabriel Kreiman, David Cox
The ability to predict future states of the environment is a central pillar of intelligence. At its core, effective prediction requires an internal model of the world and an unders…