Can neural networks systematically capture discrete, compositional task structure despite their continuous, distributed nature? The impressive capabilities of large scale neural networks suggest that the answer to this question is yes. Howe

Graph Neural Networks (GNNs) have emerged as powerful representation learning tools for capturing complex dependencies within diverse graph-structured data. Despite their success in a wide range of graph mining tasks, GNNs have raised serious concerns regarding their trustworthiness, including susceptibility to distribution shift, biases towards certain populations, and lack of explainability. Recently, integrating causal learning techniques into GNNs has sparked numerous ground-breaking studies since many

In Disguised Queries, I talked about a classification task of "bleggs" and "rubes". The typical blegg is blue, egg-shaped, furred, flexible, opaque,…

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The large range of timescales empirically observed in neural circuits can be naturally explained when neural assemblies of heterogeneous size are recurrently coupled, empowering the neural circuits to efficiently process complex time-varying input signals

Abstract page for arXiv paper 2312.04501: Graph Metanetworks for Processing Diverse Neural Architectures

Author summary The effective dimensionality of neural population codes in both brains and artificial neural networks can be far smaller than the number of neurons in the population. In vision, it has been argued that there are crucial benefits of representing images using codes that are as simple and low-dimensional as possible, allowing representations to emphasize key semantic content and attenuate irrelevant perceptual details. However, there are competing benefits of high-dimensional codes, which can ca

Aman's AI Journal | Course notes and learning material for Artificial Intelligence and Deep Learning Stanford classes.

WebNN, WebNN Neural Network API

Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity February 10, 2016 Build your own neural network classifier in R Filed under: Classifier , Neural Networks , R — Patrick Durusau @ 5:14 pm Build your own neural network classifier in R by Jun Ma. From the post: Image classification is one important field in Computer Vision, not only because so many applications are associated with it, but also a lot of Computer Vision problems can be effectively reduced to image classification. The