Abstract page for arXiv paper 1701.06538: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Average error: Start training Reset neural network Regenerate training set This is a visualisation of the three-layer feedforward neural network described in “Neural Networks That Learn”, chapter 36 of The New Turing Omnibus . It was made for the 2016-05-31 meeting of London Computation Club . The network can be trained to convert polar coordinates into rectangular coordinates . The training points are randomly chosen from the unit disk and their polar coordinates fed into the network. The network’s
# [Haskell-cafe] Spiking neural networks in Haskell Dmitry V'yal akamaus at gmail.com Sat Jul 3 07:19:32 EDT 2010 - Previous message: [Haskell-cafe] Parsec combinator like Prolog's cut operator? - Next message: [Haskell-cafe] Read Instance code. - Messages sorted by: - [ date ] - [ thread ] - [ subject ] - [ author ] Greetings anyone, Some time ago I with a friend of mine implemented a toy interactive simulator of neural network activity. It has a totally imperative design and uses Gtk2hs for user inter
Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuition. Most existing methods generate explanations by identifying a subgraph of an input graph that has a strong correlation with the prediction. These explanations are not robust to noise because independently optimizing the correlation for a single input can easily overfit noise. Moreover, they do not align well with human intuition
Video analysis relies on neural networks that process both spatial and temporal information. Three widely used architect
Ph. D. Thesis 2. Theory � Fundamentals of the Multivariate Data Analysis 2.8. Too Much Information Deteriorates Calibration 2.8.2. Neural Networks and the Complexity Problem 2.8.1. Overfitting, Underfitting and Model Complexity 2.8.2. Neural Networks and the Complexity Problem 2.8.3. Brute Force Variable Selection 2.8.4. Variable Selection by Stepwise Algorithms 2.8.5. Variable Selection by Genetic Algorithms 2.8.6. Variable Selection by Simulated Annealing 2.8.7. Variable Compression by Principal Co
Equivariant neural networks: how encoding symmetries (translation, rotation, permutation) into architecture reduces parameters, improves generalization, and connects to geometric deep learning
A technical guide to convolutional neural networks: convolution, pooling, stride, padding, classic architectures, CNNs vs vision transformers
If a neural network is <em>modular</em>, that means it consists of clusters (modules) of neurons, such that the neurons within the cluster are strongly connected to each other, but only weakly connected to the rest of the network. Making networks more modular is useful to us if the modules represent concepts which we can understand because this
Introduction Deep neural networks (DNNs) are great at fitting data, but terrible at predicting when they don’t know the right answer. Tackling this latter challenge (often called uncertainty quantification) is at the core of various problems in AI/ML. For example, reducing hallucinations in large language models, 1 or the classical balancing of exploration versus exploitation in reinforcement learning, neural bandits, and Bayesian optimization. A popular way to aim for both reliable predictions and