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Understanding the memory capacity of neural networks remains a challenging problem in implementing artificial intelligence systems. In this paper, we address the notion of capacity with respect to Hopfield networks and propose a dynamic approach to monitoring a network's capacity. We define our understanding of capacity as the maximum number of stored patterns which can be retrieved when probed by the stored patterns. Prior work in this area has presented static expressions dependent on neuron count $N$, fo
Researchers from Stanford University and NVIDIA developed Deep Compression, a multi-stage method combining pruning, trained quantization, and Huffman coding to reduce the size of deep neural
The author discusses experiments with neural networks for trading predictions, noting that using only price data yields slightly worse results than with additional indicators. They suggest that neural networks can internally generate indicators, making external ones unnecessary. The author also explores ideas like sorting trades by efficiency, using regression for self-learning systems, and addressing challenges in multi-output regression problems. They emphasize the importance of training data diversity an
Hello. I was following along with Dan’s Youtube series on neural networks and even though I’m only a beginner decided to write it with the added functionality of having multiple hidden layers instead of copying exactly w
Aller au contenu principal Nom d'utilisateur Mot de passe ATALA Association pour le Traitement Automatique des Langues Navigation principale Fil d'Ariane Accueil Classifying Semantic Clause Types With Recurrent Neural Networks: Analysis of Attention, Context & Genre Characteristics Maria Becker*, Michael Staniek*, Vivi Nastase*, Alexis Palmer** et Anette Frank* *Heidelberg University, Department of Computational Linguistics **University of North Texas, Department of Linguistics Résumé (en anglais
We use cookies to improve your experience with our site. All Title Author Keyword Abstract DOI Category Address Fund Advanced Search All Title Author Keyword Abstract DOI Category Address Fund PACS EEACC Luo Y, Wang YB, Song YC et al. TCLI: A triple-cache layer-wise inference system for reducing redundant data loading and computation in graph neural networks. JOURNAL OFCOMPUTER SCIENCE AND TECHNOLOGY, 41(2): 520−532, Mar. 2026. DOI: 10.1007/s11390-025-5554-1 Citation: Luo Y, Wang YB, Song
Jump to content Main menu Main menu move to sidebar hide Navigation Contribute Search Search Appearance Personal tools Contents move to sidebar hide (Top) 1 Architecture Toggle Architecture subsection 1.1 Convolutional layers 1.2 Pooling layers 1.3 Fully connected layers 1.4 Receptive field 1.5 Weights 1.6 Deconvolutional 2 History Toggle History subsection 2.1 Receptive fields in the visual cortex 2.2 Fukushima's analog threshold elements in a vision model 2.3 Neocognitron, origin of the trainable CNN arch
(2024) Elmoznino, Bonner. PLoS Computational Biology. Geometric descriptions of deep neural networks (DNNs) have the potential to uncover core representational principles of computational models in neuroscience. Here we examined the geometry of DNN models of visual cortex by quantifying the laten
# Neural circuits Behavior By Sarah Thau 26 August 2026 ### Wouldn’t you like to know? A mouse would Mice seek information for curiosity’s sake—and their desire for knowledge versus a payout is represented distinctly in the brain, new findings suggest. Behavior ### Wouldn’t you like to know? A mouse would Mice seek information for curiosity’s sake—and their desire for knowledge versus a payout is represented distinctly in the brain, new findings suggest. By Sarah Thau 26 August 2026 | 5 min read