Neural network, Backpropagation, Information retrieval, Plinko, Determinant, Equivalence relation

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On Dec 22, 2020
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3 Deep Symmetry Networks Deep symmetry networks represent rich compositional structure that incorporates invariance to high- dimensional symmetries. S(M [l − 1, Il−1])(P′) 〉 (3) 3 Layer l Feature map i Layer l-1 Feature maps 0,1,2 Layer l-1 Pooled feature maps 0,1,2 Kernels Figure 1: The ...

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On Dec 22, 2020
@pmddomingos shared
@BiologicalGpt @kchonyc Check out this paper: https://t.co/dP2Ou73NzW
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3 Deep Symmetry Networks Deep symmetry networks represent rich compositional structure that incorporates invariance to high- dimensional symmetries. S(M [l − 1, Il−1])(P′) 〉 (3) 3 Layer l ...

Artificial Intelligence and Deep Learning For the Extremely Confused

Artificial Intelligence and Deep Learning For the Extremely Confused

The watershed moment in Deep Learning is typically cited as 2012’s AlexNet, by Alex Krizhevsky and Geoffrey Hinton, a state of the art GPU accelerated Deep Learning network that won that ...

Demystifying Convolutional Neural Networks

Demystifying Convolutional Neural Networks

An Intuitive Explanation of Convolutional Neural Networks.

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Specifically, we do language modeling in both the forward and backward directions to pretrain the weights of NER 2 Bidirectional Language Modeling for Transfer Learning in Biomedical NER ...

Flow-based Deep Generative Models

Flow-based Deep Generative Models

In this post, we are looking into the third type of generative models: flow-based generative models. Different from GAN and VAE, they explicitly learn the pr...

Are capsules a good idea? A generative perspective

Are capsules a good idea? A generative perspective

I’ve recently written a paper on a fully probabilistic version of capsule networks. While trying to get this kind of model to work, I found some interesting conceptual issues with the ideas ...