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As a variant of Graph Neural Networks (GNNs), Unfolded GNNs offer enhanced interpretability and flexibility over traditional designs. Nevertheless, they still...

Abstract page for arXiv paper 2503.24322: NoProp: Training Neural Networks without Full Back-propagation or Full Forward-propagation

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09/07/21 - Recursive least squares (RLS) algorithms were once widely used for training small-scale neural networks, due to their fast converg...

12/28/21 - Recent works have shown that the computational efficiency of video recognition can be significantly improved by reducing the spati...

12/05/17 - Convolutional Neural Networks (CNNs) have shown a great deal of success in diverse application domains including computer vision, ...

Become acquainted with two Deep Learning techniques that work in tandem to learn artificial neural network parameters.

In the acceleration of deep neural network training, the graphics processing unit (GPU) has become the mainstream platform. GPUs face substantial challenges on...

04/06/16 - In this work we investigate the problem of road scene semantic segmentation using Deconvolutional Networks (DNs). Several constrai...

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We show that model-free perturbative methods can be used to efficiently train modern neural network architectures in a way that can be directly applied to emerg

Receiver operating characteristic (ROC) curve is an informative tool in binary classification and Area Under ROC Curve (AUC) is a popular metric for reportin...

Deep Neural Networks (DNNs) are successful but work as black-boxes. Elucidating their inner workings is crucial but a difficult task. In this work, we...

Abstract page for arXiv paper 1708.07120: Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates

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Graphcore Research is exploring novel ways to train neural networks that could allow us to scale to substantially larger models in future.

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CCNP Enterprise Training in Bangalore: Advancing Skills for Modern Enterprise Networks - homepage

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Abstract page for arXiv paper cs/0507039v1: Distributed Regression in Sensor Networks: Training Distributively with Alternating Projections

09/20/21 - Embedding computation in biochemical environments incompatible with traditional electronics is expected to have wide-ranging impac...

With the arrival of new hardware specifically designed for machine intelligence, like Graphcore’s Intelligence Processing Unit (IPU), it’s time to rethink...

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Chainer Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.) - wkentaro/fcn

Deep learning practitioners typically rely on training and validation loss curves to understand neural network training dynamics. This blog post demonstrates...