Posts about convolutional neural network written by Tinniam V Ganesh
This paper identifies and characterizes 'over-squashing' as a distinct and critical bottleneck in Graph Neural Networks (GNNs) that limits their ability to capture long-range dependencies. The work
HTM Forum Sparse Networks from Scratch: Faster Training without Losing Performance mraptor August 31, 2021, 2:50pm 1 Tim Dettmers – 11 Jul 19 Sparse Networks from Scratch: Faster Training without Losing Performance This blog post explains the sparse momentum algorithm and how it enables the fast training of sparse networks to dense performance levels — sparse learning. 2 Likes SeanOConnor September 1, 2021, 11:49pm 2 I like the overview of sparse neural networks here: https://youtu.be/H7-p3OWPpEI And
Model compression is crucial for deployment of neural networks on devices with limited computational and memory resources. Many different methods show comparable accuracy of the compressed model and similar compression rates. However, the majority of the compression methods are based on heuristics and offer no worst-case guarantees on the trade-off between the compression rate and the approximation error for an arbitrarily new sample. We propose the first efficient structured pruning algorithm with a provab
Skip to content Quote Investigator® Tracing Quotations Back Quote Origin: It May Be That Today’s Large Neural Networks Are Slightly Conscious Posted by quoteresearch October 5, 2022March 14, 2025 Ilya Sutskever? Blaise Agüera y Arcas? Yann LeCun? Blake Lemoine? Apocryphal? Question for Quote Investigator: Apparently, a top researcher in artificial intelligence (AI) controversially suggested in early 2022 that contemporary digital neural networks employed in AI systems might be “slightly conscious
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We discuss convolutional neural networks, a deep learning model inspired by the human visual system that has rocked the state-of-the-art in computer vision tasks
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# How to Avoid Overfitting in Deep Learning Neural Networks Training a deep neural network that can generalize well to new data is a challenging problem. A model with too little capacity cannot learn the problem, whereas a model with too much capacity can learn it too well and overfit the training dataset. Both cases result in a model that does not generalize well. A modern approach to reducing generalization error is to use a larger model that may be required to use regularization during training that k
Abstract page for arXiv paper 1904.07773: Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation