207 citations · 516 across the 63 of their papers we have counts for
6 papers · 1 filter
Improving Sparse Associative Memories by Escaping from Bogus Fixed Points
Zhe Yao, Vincent Gripon, Michael Rabbat
The Gripon-Berrou neural network (GBNN) is a recently invented recurrent neural network embracing a LDPC-like sparse encoding setup which makes it extremely resilient to noise and…
A study of retrieval algorithms of sparse messages in networks of neural cliques
Ala Aboudib, Vincent Gripon, Xiaoran Jiang
Associative memories are data structures addressed using part of the content rather than an index. They offer good fault reliability and biological plausibility. Among different fa…
A Massively Parallel Associative Memory Based on Sparse Neural Networks
Zhe Yao, Vincent Gripon, Michael G. Rabbat
Associative memories store content in such a way that the content can be later retrieved by presenting the memory with a small portion of the content, rather than presenting the me…
A Low-Power Content-Addressable-Memory Based on Clustered-Sparse-Networks
Hooman Jarollahi, Vincent Gripon, Naoya Onizawa +1
A low-power Content-Addressable-Memory (CAM) is introduced employing a new mechanism for associativity between the input tags and the corresponding address of the output data. The…
Reconstructing a Graph from Path Traces
Vincent Gripon, Michael Rabbat
This paper considers the problem of inferring the structure of a network from indirect observations. Each observation (a "trace") is the unordered set of nodes which are activated…
Maximum Likelihood Associative Memories
Vincent Gripon, Michael Rabbat
Associative memories are structures that store data in such a way that it can later be retrieved given only a part of its content -- a sort-of error/erasure-resilience property. Th…