Jul 3, 2022 · This paper shows how such an approach can be realised in Haskell, by encoding neural networks as recursive data types, and then their training ...
Sep 22, 2022 · This paper shows how such an approach can be realised in Haskell, by encoding neural networks as recursive data types, and then their training as recursion ...
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Aug 27, 2021 · We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops.
Jun 14, 2022 · The darker areas in the right hand plot are where the input space was folded over itself (the grid points are plotted with transparency).
Sep 22, 2022 · Dive into the research topics of 'Folding over Neural Networks'. Together they form a unique fingerprint. Neural Network Computer Science 100%.
Sep 9, 2024 · This paper shows how such an approach can be realised in Haskell, by encoding neural networks as recursive data types, and then their training ...
Jul 26, 2019 · The folded version is a more compact description, whereas the unfolded version makes it more clear that the hidden state takes on multiple values over time.
Sep 26, 2022 · This paper shows how such an approach can be realised in Haskell, by encoding neural networks as recursive data types, and then their training ...
Oct 3, 2023 · Here we describe the development of SeqPredNN, a feed-forward neural network trained with X-ray crystallographic structures from the RCSB ...
Sep 17, 2014 · We are writing a small ANN which is supposed to categorize 7000 products into 7 classes based on 10 input variables. In order to do this we have to use k-fold ...