A lot of “capture-the-flag” style ML puzzles give you a black box neural net, and your job is to figure out what it does. When we were thinking of creating our own ML puzzle early last year, we wanted to do something a little different. We thought it’d be neat to give users a complete specification of the neural net, weights and all. They would then be forced to use the tools of mechanistic interpretability to reverse engineer the network—which is a situation we sometimes find ourselves facing in

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

Symbolic reasoning and neural networks are often considered incompatible approaches. Connectionist models known as Vector Symbolic Architectures (VSAs) can potentially bridge this gap. However, classical VSAs and neural networks are still considered incompatible. VSAs encode symbols by dense pseudo-random vectors, where information is distributed throughout the entire neuron population. Neural networks encode features locally, often forming sparse vectors of neural activation. Following Rachkovskij (2001

where Innovation meets Impact

A computer looked at millions of human faces, studied every detail, and then invented a person who has never existed. No photograph. No model. Just math — and

Discover how Jeffrey Elman's simple recurrent networks function. These groundbreaking AI models learn to find hidden grammar and structure in language, all without explicit rules

A major interest in brain research is understanding behavioral experiences at the synaptic and circuit level. However, gaining insight into the neural circuits that underlie a specific behavioral experience is a challenge. The perplexity lies in defining which of the large number of neuronal inputs a specific region of the brain receives expresses a defined behavior, and yet there are no direct methods available to overcome this challenge

The Nervana NNP is designed from the ground up with this type of computing in mind.

“Liquid” neural nets, based on a worm’s nervous system, can transform their underlying algorithms on the fly, giving them unprecedented speed and adaptability.Artificial intelligence researchers have celebrated a string of successes with neural networks, computer programs that roughly mimic how our brains are organized. But despite rapid progress, neural networks remain relatively inflexible, with little ability

Chainer latest Tutorials Examples Neural Net Examples MNIST using Trainer MNIST with a Manual Training Loop Convolutional Network for Visual Recognition Tasks DCGAN: Generate images with Deep Convolutional GAN Recurrent Nets and their Computational Graph RNN Language Models Word2Vec: Obtain word embeddings Write a Sequence to Sequence (seq2seq) Model References Other Community Chainer Docs » Neural Net Examples Edit on GitHub Neural Net Examples ¶ MNIST using Trainer MNIST with a Manual Training Loop