785 citations · 907 across the 9 of their papers we have counts for
12 papers
On the special role of class-selective neurons in early training
Omkar Ranadive, Nikhil Thakurdesai, Ari S Morcos +2
It is commonly observed that deep networks trained for classification exhibit class-selective neurons in their early and intermediate layers. Intriguingly, recent studies have show…
Blockwise Self-Supervised Learning at Scale
Shoaib Ahmed Siddiqui, David Krueger, Yann LeCun +1
Current state-of-the-art deep networks are all powered by backpropagation. In this paper, we explore alternatives to full backpropagation in the form of blockwise learning rules, l…
Progress and limitations of deep networks to recognize objects in unusual poses
Amro Abbas, Stéphane Deny
Deep networks should be robust to rare events if they are to be successfully deployed in high-stakes real-world applications (e.g., self-driving cars). Here we study the capability…
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Jure Zbontar, Li Jing, Ishan Misra +2
Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large computer vision benchmarks. A successful approach to SSL is to learn embeddings which are…
Addressing the Topological Defects of Disentanglement via Distributed Operators
Diane Bouchacourt, Mark Ibrahim, Stéphane Deny
A core challenge in Machine Learning is to learn to disentangle natural factors of variation in data (e.g. object shape vs. pose). A popular approach to disentanglement consists in…
Predicting synchronous firing of large neural populations from sequential recordings
Oleksandr Sorochynskyi, Stéphane Deny, Olivier Marre +1
A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing…