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A description of batch normalization and residual networks which are commonly used in neural networks

Interpretable Neural-Symbolic Concept ReasoningPietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Ch

Comparison of Statistical Learning Networks and Artificial Neuronets

I recently posted an article featuring a very deep neural network in action (250 layers), see here. Each frame in the video represented one layer, with the signal propagating from one layer to the next. In the last layer, the whole space was classified, in the sense that any new observation was immediately assigned to

I will now provide the categorical foundation of the Haskell implementation from the previous post. A PDF version that contains both parts is also available. The Para Construction There's been a lot of interest in categorical foundations of deep learning. The basic idea is that of a parametric category, in which morphisms are parameterized by…

Skip to content Neural network analysis “unequivocally” reveals threshold dose response in atomic bomb victims By Rod Adams October 28, 2014July 11, 2020 Low dose radiation suppresses cancer. Note: Low dose in this case is defined as being below a threshold value of somewhere between 100 – 200 mSv depending on exposed organ. That bold, conventional wisdom-challenging statement is supported by an incredibly important paper titled Cancer risk at low doses of ionizing radiation: artificial neural

Understand what generative AI is, how it differs from traditional AI, and the journey from rule-based systems through statistical methods to modern neural network generation

The attention mechanism allows us to merge a variable-length sequence of vectors into a fixed-size context vector. What if we could use this mechanism to entirely replace recurrence for sequential modeling? This blog post covers the Transformer architecture which explores such an approach.

I have created a knowledgebase/graph neural network architecture (GIAANN prototype) which can be used to predict the next token in a sequence. It trains a set of columns for every new noun encountered in a textual corpus