Abstract page for arXiv paper 2605.31315: Graph Neural Networks Are Not Continuous Across Graph Resolutions
The human brain, with its remarkable general intelligence and exceptional efficiency in power consumption, serves as a constant inspiration and aspiration for the field of artificial intelligence. Drawing insights from the ...
Research on Deep Neural Networks (DNNs) has focused on improving performance and accuracy for real-world deployments, leading to new models, such as Spiking Neural Networks (SNNs), and optimization techniques, e.g., quantization and pruning for compressed networks. However, the deployment of these innovative models and optimization techniques introduces possible reliability issues, which is a pillar for DNNs to be widely used in safety-critical applications, e.g., autonomous driving. Moreover, scaling techn
nan loss, why did it happen, what went wrong?
Introduction to Deep Learning Neural Networks
A short reference on the neural-network vocabulary used across the neural texture, neural material and neural appearance posts — MLP, weights and biases, ReLU, forward pass, loss, gradient descent, backpropagation, Adam, latent vectors, feature grids and decoder networks — explained once, concretely, so those posts can link here instead of re-deriving it
Sid Black*, Lee Sharkey*, Leo Grinsztajn, Eric Winsor, Dan Braun, Jacob Merizian, Kip Parker, Carlos Ramón Guevara, Beren Millidge, Gabriel Alfour, C…
Skip to main content Breadcrumb Papers A Theoretical Framework for Inference and Learning in Predictive Coding Networks A Theoretical Framework for Inference and Learning in Predictive Coding Networks Millidge B Song Y Salvatori T Lukasiewicz T Bogacz R This paper analyses a relationship between a model of learning in the brain (called predictive coding), and an algorithm for training artificial neural networks (called target propagation). Scientific Abstract Predictive coding (PC) is an influential theory
← Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes → # Global Prediction of COVID-19 Variant Emergence Using Dynamics-Informed Graph Neural Networks 投稿日: 2024年12月30日 作成者: jarxiv 新型コロナウイルス感染症（COVID-19）のパンデミック中
NeurIPS Proceedings Search Modular Networks: Learning to Decompose Neural Computation Louis Kirsch, Julius Kunze, David Barber Advances in Neural Information Processing Systems 31 (NeurIPS 2018) Abstract Scaling model capacity has been vital in the success of deep learning. For a typical network, necessary compute resources and training time grow dramatically with model size. Conditional computation is a promising way to increase the number of parameters with a relatively small increase in resources. We pro