Recent studies have shifted their focus towards formulating traffic forecasting as a spatio-temporal graph modeling problem. Typically, they constructed a static spatial graph at each time step and then connected each node with itself between adjacent time steps to create a spatio-temporal graph. However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks. Additionally, those models overl
0 evaluations Latest version Jun 29, 2026
Biological neural networks (i.e. brains) and artificial neural networks have sufficient commonalities that it's often reasonable to treat our knowled
Find the latest research papers and news in Gene regulatory networks. Read stories and opinions from top researchers in our research community
This paper explores the applicability of Adaptive Resistance Theory- (ART-) type neural networks for finding and encoding linguistic structures, specifically those corresponding to acoustic patterns in natural speech. We build an interpretation of human perceptual response to acoustic pattern in natural speech, translating this to a neural architecture as a model of acquisition, storage, and classification of acoustic speech patterns
Neural networks are computer learning algorithms that mimic the interconnected neurons of a living brain, managing astonishing feats of image classification, speech recognition, or music generation by forming connections between simulated neurons.I’m not a neural network researcher, but there’s never been a better time to experiment with them, thanks to open-source packages like torch, a scientific computing framework with built-in neural network capabilities. Inspired by T
Skip to main content Toggle navigation Lipman’s Artificial Intelligence Directory [Now Reading] Qualitatively Characterizing Neural Network Optimization Problems January 29, 2018January 29, 2018 Juan Miguel Valverde Papers Title: Qualitatively characterizing neural network optimization problems Authors: Ian J. Goodfellow, Oriol Vinyals, Andrew M. Saxe Link: https://arxiv.org/abs/1412.6544 Quick Summary: The main goal of the paper is to introduce a simple way to look at the trajectory of the weights
This website is the home of Harsha Kokel. A Ph.D. student working with Prof. Sriraam Natarajan at The University of Texas at Dallas
Blog Topics Advertise Join Newsletter Neural network AI is simple. So… Stop pretending you are a genius This post may come off as a rant, but that’s not so much its intent, as it is to point out why we went from having very few AI experts, to having so many in so little time. --> comments By Brandon Wirtz , CEO and Founder at Recognant On a regular basis people tell me about their impressive achievements using AI. 99% of these things are completely stupid. This post may come off as a rant, but that’s
Skip to content TheLinuxCode Software Menu Toggle Distros Menu Toggle SysAdmin Menu Toggle Residential Proxies Residential Proxies TheLinuxCode Main Menu Menu Neural Network vs Deep Learning System: What Really Changes When You Add Depth Leave a Comment / By Linux Code / February 4, 2026 You’ve probably seen the phrase “neural network” used as a catch-all for anything that looks like AI. In real projects, that sloppy wording costs time: it changes how you plan data collection, how you budget compute