Support Vector Machines, Neural Networks and Fuzzy Logic Models - Preface
When equipped with efficient optimization algorithms, the over-parameterized neural networks have demonstrated high level of performance even though the loss function is non-convex and non-smooth. While many works have been focusing on understanding the loss dynamics by training neural networks with the gradient descent (GD), in this work, we consider a broad class of optimization algorithms that are commonly used in practice. For example, we show from a dynamical system perspective that the Heavy Ball (HB
Traditional software development has established four stages of testing software: Unit testing, integration testing, system testing, and...
Rigorous treatment of CNNs: convolution as cross-correlation, weight sharing, translation equivariance, pooling, receptive fields, and parameter counting.
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Building an image recognition app using Metal
# Visualizing Neural Networks with the Grand Tour The Grand Tour in action. This visualization shows the behavior of the final 10-dimensional layer of a neural network as it is trained on the MNIST dataset. With this technique, it is possible to see interesting training behavior. For example, the network appears to learn to classify digits 1 and 7 in an almost discontinuous manner, after training epochs 14 and 21 respectively. ### Authors ### Affiliations Mingwei Li University of Arizona Zhenge Zhao
Abstract page for arXiv paper 1902.01028: Can SGD Learn Recurrent Neural Networks with Provable Generalization
For anyone interested, a new version (v.0.1.4) of nnlib2Rcpp is available on GitHub. It can be installed the usual way for packages on GitHub: library(devtools) install_github("VNNikolaidis/nnlib2Rcpp") nnlib2Rcpp is an R package containing a number of Neural Network (NN) implementations. The NNs are implemented in C++ (using nnlib2 C++ class library) and are interfaced with R … Continue reading Version 0.1.4 of nnlib2Rcpp: a(nother) R package for Neural Networks
NeurIPS Proceedings Search MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superposition Nicolas Menet, Michael Hersche, Geethan Karunaratne, Luca Benini, Abu Sebastian, Abbas Rahimi Advances in Neural Information Processing Systems 36 (NeurIPS 2023) Main Conference Track Abstract With the advent of deep learning, progressively larger neural networks have been designed to solve complex tasks. We take advantage of these capacity-rich models to lower the cost of inference by