Generative Flow Networks (GFlowNets, GFNs) are a generative framework for learning unnormalized probability mass functions over discrete spaces. Since their inception, GFlowNets have proven to be useful for learning generative models in applications where the majority of the discrete space is unvisited during training. This has inspired some to hypothesize that GFlowNets, when paired with deep neural networks (DNNs), have favourable generalization properties. In this work, we empirically verify some of the

Not sure I read this correctly, it seems pretty drastic. What could be a mechanism for such replication? Anyone understands this theory of “critical branching networks”? Brief write-up: https://neurosciencenews.com/f

Skip to content Hackaday Primary Menu Search for: August 9, 2026 Physical Neural Network Can Be Trained Like A Digital One 11 Comments by: Donald Papp July 20, 2023 Title: Copy Short Link: Copy Here’s an unusual concept: a computer-guided mechanical neural network (video, embedded below.) Why would one want a mechanical neural network? It’s essentially a tool to explore what it would take to make physical materials work in nonstandard ways. The main part is a lattice of interlinked mechanical components

If you follow AI you might have heard about the advent of the potentially revolutionary Capsule Networks. I will show you how you can start using them today

Learn about Generative Adversarial Networks (GANs) and obtain a great foundation on cutting edge GANs and how GANs are used to generate incredible results

Research led by Queen Mary University of London, proposes a novel 'higher-order' Kuramoto model that combines topology with dynamical systems and characterises synchronization in higher-order networks for the first time

Pointer Networks, developed by researchers at Google Brain and UC Berkeley, introduce a novel architecture that adapts attention mechanisms to select elements directly from the input sequence as

How the Neural Engine inside M1 through M5 chips accelerates Whisper transcription, and the real performance gains over CPU-only inference

How to Choose the Right Regularization Technique Selecting the appropriate regularization technique is crucial for optimizing neural network training speed

NeuPy is a Python library for Artificial Neural Networks. NeuPy supports many different types of Neural Networks from a simple perceptron to deep learning models