NeurIPS 2019 Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center Paper ID: 9024 Title: Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks Reviewer 1 Originality: the use use of the Legendre polynomial seems rather creative, it was certainly important to define RNNs with good models of coupled linear units. Quality: The set of benchmarks is well chosen to describe a broad scope of qualities that RNN require. One non-artificial task would have been a plus thou
NeurIPS 2019 Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center Paper ID: 9024 Title: Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks Reviewer 1 Originality: the use use of the Legendre polynomial seems rather creative, it was certainly important to define RNNs with good models of coupled linear units. Quality: The set of benchmarks is well chosen to describe a broad scope of qualities that RNN require. One non-artificial task would have been a plus thou
Python, PyTorch
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural NetworksJuho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek
An interactive guide to saddle-to-saddle learning in recurrent neural networks, via pole-zero geometry in the complex plane
(Pie -> cat courtesy of https://affinelayer.com/pixsrv/ ) I work with neural networks, which are a type of machine learning computer program that learn by looking at examples. They’re used for all sorts of serious applications, like facial recognition and ad targeting and language translation. I, however, give them silly datasets and ask them to do their best
Open Menu Journal of Language Modelling About Search Home / Archives / Vol. 8 No. 1 (2020) / Articles Neural network models for phonology and phonetics Authors Keywords: phonology, neural networks, speech perception, historical linguistics Abstract This paper argues that if phonological and phonetic phenomena found in language data and in experimental data all have to be accounted for within a single framework, then that framework will have to be based on neural networks. We introduce an artificial neural n
Deep neural networks with short residual connections have demonstrated remarkable success across domains, but increasing depth often introduces computational redundancy
Unravelling The Complexity of Neural Network For Beginners
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