A Neural Network Gaussian Process (NNGP) is a Gaussian process (GP) obtained as the limit of a certain type of sequence of neural networks. Specifically...

Bayesian neural networks reduce to a Gaussian process with a closed form compositional kernel. This Gaussian process is called the Neural Network Gaussian Process...

since finite width neural networks often perform strictly better as layer width is increased. The Neural Network Gaussian Process (NNGP) corresponds to...

emerge: At initialization (before training), the neural network ensemble is a zero-mean Gaussian process (GP). This means that distribution of functions...

surpassing human expert performance. Early forms of neural networks were inspired by information processing and distributed communication nodes in biological...

In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function is an activation function defined as the...

Radial basis function kernel (RBF) String kernels Neural tangent kernel Neural network Gaussian process (NNGP) kernel Kernel methods for vector output Kernel...

space and foregoing the need to query a neural network for each point. Instead, simply "splat" all the gaussians onto the screen and they overlap to produce...

types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate...

electronics and signal processing, mainly in digital signal processing, a Gaussian filter is a filter whose impulse response is a Gaussian function (or an approximation...

machine learning, Gaussian process approximation is a computational method that accelerates inference tasks in the context of a Gaussian process model, most...

for further processing depending on the input. One of its two networks has "fast weights" or "dynamic links" (1981). A slow neural network learns by gradient...

Chatzis, S. P.; Demiris, Y. (2011). "Echo State Gaussian Process". IEEE Transactions on Neural Networks. 22 (9): 1435–1445. doi:10.1109/TNN.2011.2162109...

developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's...

visual operations. Gaussian functions are used to define some types of artificial neural networks. In fluorescence microscopy a 2D Gaussian function is used...

learning, cellular neural networks (CNN) or cellular nonlinear networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference...

high-dimensional statistics. Random matrix theory also saw applications in neural networks and deep learning, with recent work utilizing random matrices to show...

involve training a neural network to sequentially denoise images blurred with Gaussian noise. The model is trained to reverse the process of adding noise...

neural networks, marking a departure from the typical focus on learning mappings between finite-dimensional Euclidean spaces or finite sets. Neural operators...

of statistical analysis software that allows doing inference with Gaussian processes often using approximations. This article is written from the point...

algorithms, including: Kalman filter Bayesian networks Dempster–Shafer Convolutional neural network Gaussian processes Two example sensor fusion calculations...

potentials by significantly constraining the neural network search space. Other models use a similar process but emphasize bonds over atoms, using pair...

Neural coding (or neural representation) is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the...

with the objective of removing successive applications of noise (commonly Gaussian) on training images. The LDM is an improvement on standard DM by performing...

The activation function of a node in an artificial neural network is a function that calculates the output of the node based on its individual inputs and...

M (2015). "Learning Bayesian Networks with Thousands of Variables". NIPS-15: Advances in Neural Information Processing Systems. Vol. 28. Curran Associates...

Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory...

the network. Localist attractor networks encode knowledge locally by implementing an expectation–maximization algorithm on a mixture-of-gaussians representing...

dedicated to processing sensory functions, for different parts of the body. Self-organizing maps, like most artificial neural networks, operate in two...

AlexNet is a convolutional neural network architecture developed for image classification tasks, notably achieving prominence through its performance in...