Corr I 481<br /> Networks/Shoda/Smith: One notabl
Read about RBC Borealis's publication on Gumbel-Softmax Selective Networks. Explore the latest advancements in deep learning and AI technology
Backpropagation is the algorithm for efficiently computing gradients in neural networks by applying the chain rule in reverse, enabling multi-layer network training
--> The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks Sebastian Bieringer, Gregor Kasieczka, Maximilian F. Steffen, Mathias Trabs. Year: 2026, Volume: 27 , Issue: 1, Pages: 1−50 Abstract MALA is a popular gradient-based Markov chain Monte Carlo method to access the Gibbs-posterior distribution. Stochastic MALA (sMALA) scales to large data sets, but changes the target distribution from the Gibbs-posterior to a surrogate posterior which only
ACL Anthology About Announcements Communication channels Related work Copyright Credits Volunteer Development Feedback Using Citing papers Links in the Anthology Data access All FAQs Details Anthology identifiers Names ORCID iDs DOIs Verified authors Contributions Submissions Corrections Author pages Attachments GitHub BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (2023) Volumes Show all abstractsHide all abstracts up pdf (full) bib (full) Proceedings of the 6th BlackboxNLP Workshop: Analy
It’s hard to come up with ideas for Halloween costumes, especially when it seems like all the good ones are taken. And don’t you hate showing up at a party only to discover that there’s *another* pajama cardinalfish? I train neural networks, a type of machine learning algorithm, to write humor by giving them datasets that they have to teach themselves to mimic. They can sometimes do a surprisingly good job, coming up with
Spiking neurons underlie the brain's extreme energy efficiency, and therefore have great potential in neuromorphic computing, although realising this efficiency...
# 2.2. Overview of Neural Network Training To obtain the appropriate parameter values for neural networks, we can use optimization techniques. Here is an overview of how optimization techniques are used in neural networks: Determine the loss function. The loss function, also known as the error function, measures the difference between the network’s output and the desired output (labels). A lower loss value indicates a closer match between the network’s prediction and the actual label. Common choices inc
And here, friends, is the same list of networks I provided earlier in the table of contents for my forthcoming Other Networks: A Radical Technology Sourcebook but listed chronologically. Of course the list is impossibly incomplete but it still reveals some interesting lulls and surges of network activity. Also note that I have posted a
Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct contexts and objects during natural behavior. We posit that this ability to generalize arises from a specific representational geometry, that we call abstract and that is referred to as disentangled in machine learning. These abstract representations have been observed in recent neurophysiological studies. However, it is unknown how they emerge. Here, using feedforward neural networks, we demonstrate that the l