[color=rgba(83, 100, 121, 0.9)]16 August 2020
TLDR
These models are effective descriptions of a more microscopic theory that has additional (hidden) neurons and only requires two-body interactions between them and are a valid model of large associative memory with a degree of biological plausibility.Expand
[color=rgba(83, 100, 121, 0.9)]14 January 2020
TLDR
This work proposes a novel hashing algorithm BioHash that produces sparse high dimensional hash codes in a data-driven manner and shows that BioHash outperforms previously published benchmarks for various hashing methods.Expand
[color=rgba(83, 100, 121, 0.9)]14 August 2019
TLDR
The design of a local algorithm that can learn convolutional filters at scale on large image datasets and a successful transfer of learned representations between CIFAR-10 and ImageNet 32x32 datasets hint at the possibility that local unsupervised training might be a powerful tool for learning general representations (without specifying the task) directly from unlabeled data.Expand
A learning algorithm is designed that utilizes global inhibition in the hidden layer and is capable of learning early feature detectors in a completely unsupervised way, and which is motivated by Hebb’s idea that change of the synapse strength should be local.Expand
[color=rgba(83, 100, 121, 0.9)]4 January 2017
TLDR
DAMs with higher-order energy functions are more robust to adversarial and rubbish inputs than DNNs with rectified linear units and open up the possibility of using higher- order models for detecting and stopping malicious adversarial attacks.Expand
The proposed duality makes it possible to apply energy-based intuition from associative memory to analyze computational properties of neural networks with unusual activation functions - the higher rectified polynomials which until now have not been used in deep learning.Expand
[color=rgba(83, 100, 121, 0.9)]1 October 2015
TLDR
How the emergent computational dynamics of a biologically based neural network generates a robust natural solution to the problem of categorizing time-varying stimulus patterns such as spoken words or animal stereotypical behaviors is described.Expand
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