This project provides an interactive demonstration of CNN-JEPA (Convolutional Neural Network Joint-Embedding Predictive Architecture), a PhD-level Artificial Machine Intelligence (AMI) that showcases ...

Graph neural networks have -enabled the application of deep learning to problems that can be described by graphs, which are found throughout the different fields of sci-ence, from physics to biology, ...

High-level visual cortex and leading neural network models of the visual system retain information about multiple visual scene variables in independent, non-interfering dimensions of their population ...

High-level visual cortex and leading neural network models of the visual system retain information about multiple visual scene variables in independent, non-interfering dimensions of their population ...

The experiments demonstrate 1) the significant correlation and similarity of the semantics between the visual representations in FBNs and those in a variety of convolutional neural networks (CNNs) ...

Deep neural networks trained on large annotated visual datasets have become state-of-the-art models for both visual recognition tasks and predicting neuronal responses in the primate visual stream.

TensorFlow GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. This Python library enables GNN training and inference on graph-structured data by utilizing heterogeneous ...

This article assumes you have a basic familiarity with Python and the PyTorch neural network library. If you're new to PyTorch, you can get up to speed by reviewing the article " Multi-Class ...