Master backpropagation — the chain rule applied to computational graphs, gradient flow through layers, and why it enables deep learning.
The unified framework behind GCN, GAT, GraphSAGE, and GIN. Learn aggregation, update functions, and attention mechanisms in GNNs.
NeurIPS Proceedings Search Variational Learning for Recurrent Spiking Networks Danilo J. Rezende, Daan Wierstra, Wulfram Gerstner Advances in Neural Information Processing Systems 24 (NIPS 2011) Abstract We derive a plausible learning rule updating the synaptic efficacies for feedforward, feedback and lateral connections between observed and latent neurons. Operating in the context of a generative model for distributions of spike sequences, the learning mechanism is derived from variational inference princi
How to build neural networks that generalize well
Skip to main content NICK TASIOS Rust vs C++ - Implementing a Neural Network Nick Tasios 2019-07-06 19:18 I first learned Rust back in 2014, before it was stable. Rust is definitely a very interesting language so I have decided to revisit it by programming a simple neural network. For comparison, I also implemented the network in C++, the language I'm looking to replace. I like learning programming languages that use constructs or paradigms that are fundamentally different from what I have seen before. Rust
田中専務 拓海先生、最近の論文で「暗黙的生成事前分布」って言葉を見かけまして、部下から導入の話が出てきて困って…
Convolutional networks are ubiquitous in deep learning. They are particularly useful for images, as they reduce the number of parameters, reduce training time, and increase accuracy. However, as a model of the brain they are seriously problematic, since they require weight sharing - something real neurons simply cannot do. Consequently, while neurons in the brain can be locally connected (one of the features of convolutional networks), they cannot be convolutional. Locally connected but non-convolutional ne
Abstract page for arXiv paper 1802.08435: Efficient Neural Audio Synthesis
(2026) Mesinovic et al. Healthcare artificial intelligence systems often degrade in performance when deployed across institutions, with documented performance drops and perpetuation of discriminatory patterns embedded in data. This brittleness comes, in part, from learning statistical association...
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