Neural network, Computational neuroscience, Connectionism, Unsupervised learning, Machine learning, Neural networks
LeCun's 2022 paper on autonomous machine intelligence rehashes but does not cite essential work of 1990-2015
I am referring the interested reader again to (I) our "cognitive architectures in which all modules are differentiable and many of them are trainable" [HAB][PHD][AC90][AC90b][AC][HRL0-2][PLAN2-5], (II) our "hierarchical architecture for predictive world models that learn representations ...
LeCun's 2022 paper on autonomous machine intelligence rehashes but does not cite essential work of 1990-2015
I am referring the interested reader again to (I) our "cognitive architectures in which all modules are differentiable and many of them are trainable" ...
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26 March 1991: Neural nets learn to program neural nets with fast weights—like today's Transformer variants. 2021: New stuff!
FWPs can solve the famous vanishing gradient problem aka deep learning problem (analyzed a few months later in 1991[VAN1]) through additive fast weight changes (Sec. 1991: NNs learn to ...
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Despite the current popularity of machine learning, I haven’t found any short introductions to it which quite match the way I prefer to introduce people to the field. So here’s my own. ...
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Awesome Deep Learning Resources
Contribute to souravstat/Curated-Resources development by creating an account on GitHub.
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In this blog post, I want to share the 8 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.
Evolution of Deep learning models
Scope and approach No taxonomy of Deep learning models exists. And I do not attempt to create one here either. Instead, I explore the evolution of Deep learnin…
Top 10 Arxiv Papers Today in Computer Science
alxndrkalinin: RT @cwcyau: If you were like me and found the Neural Processes papers (https://t.co/V6swjXuDw6, https://t.co/jqI6IFb74Z) quite challenging… kastnerkyle: RT @cwcyau: If you ...