Understanding the neural mechanisms underlying associative threat learning is essential for advancing behavioral models of threat and adaptation. We investigated distinct activation patterns across ...
Recent advances in high-throughput microbiome profiling have generated expansive data sets that offer unprecedented ...
Iblis: Let me stop you right there. I agree humans can, in controlled situations, provide correct answers to math problems. I deny that they truly understand math. I had a conversation with one of ...
Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...
Neural networks first treat sentences like puzzles solved by word order, but once they read enough, a tipping point sends them diving into word meaning instead—an abrupt “phase transition” reminiscent ...
Abstract: Recently, deep neural network-based methods have been proposed for vehicle behavior prediction (VBP). However, existing methods lack an enhanced discriminative mechanism and robust learning ...
A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...
On Thursday, Google and the Computer History Museum (CHM) jointly released the source code for AlexNet, the convolutional neural network (CNN) that many credit with transforming the AI field in 2012 ...
A popular Food Network TV show will highlight two “unbelievable” Upstate New York restaurants this week. Guy Fieri’s “Diners, Drive-Ins and Dives: Triple D Nation” will showcase Eva’s European Sweets ...
Abstract: Editor’s notes: This article reviews some of the first experiments on deep neural networks to analyze the propagation of soft errors from hardware to the ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback