#dnns — Public Fediverse posts

I am really proud to announce that our latest effort on showing the advantages of our analog/digital #neuromorphic spiking neural network chips in solving complex biomedical applications has just been published here: https://rdcu.be/ef5N0

The demonstrates that *small* *highly variable* and *low accuracy* #SNNs can indeed be useful, without having to resort to #backprop in large-scale #DNNs! 😉

AI in Remote Sensing Prone to Exploitation, Study Warns of Digital and Physical Risks https://thecyberexpress.com/remote-sensing-ai-reliability/ #remotesensingapplications #FastGradientSignMethod #TheCyberExpressNews #DeepNeuralNetworks #Vulnerabilities #TheCyberExpress #FirewallDaily #remotesensing #AItechnology #AIsecurity #CyberNews #AIsystems #AImodels #DNNs

'Densely Connected G-invariant Deep Neural Networks with Signed Permutation Representations', by Devanshu Agrawal, James Ostrowski.

http://jmlr.org/papers/v24/23-0294.html

#representations #dnns #dnn

'Integrating Random Effects in Deep Neural Networks', by Giora Simchoni, Saharon Rosset.

http://jmlr.org/papers/v24/22-0501.html

#dnns #dnn #deep

Why #DeepNeuralNetworks need #Logic:

Nick Shea (#UCL/#Oxford) suggests

(1) Generating novel stuff (e.g., #Dalle's art, #GPT's writing) is cool, but slow and inconsistent.

(2) Just a handful of logical inferences can be used *across* loads of situations (e.g., #modusPonens works the same way every time).

So (3) by #learning Logic, #DNNs would be able to recycle a few logical moves on a MASSIVE number of problems (rather than generate a novel solution from scratch for each one).

#CompSci #AI

'Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces', by Masaaki Imaizumi, Kenji Fukumizu.

http://jmlr.org/papers/v23/21-0542.html

#dnns #dnn #singularity

Floating Gate #Transistor Accurate Digital in‐Memory Computing for #DNNs

Paper
https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202200127