of artificial neural networks (ANNs), the neural tangent kernel (NTK) is a kernel that describes the evolution of deep artificial neural networks during...

Fisher kernel Graph kernels Kernel smoother Polynomial kernel Radial basis function kernel (RBF) String kernels Neural tangent kernel Neural network...

artificial neural networks after random initialization of their parameters, but before training; it appears as a term in neural tangent kernel prediction...

Finland. (Known as NTK Nakkila) Neural tangent kernel, a mathematical tool to describe the training of artificial neural networks NTK, a Niterra brand of...

A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep...

architecture and initializations hyper-parameters. The Neural Tangent Kernel describes the evolution of neural network predictions during gradient descent training...

Clement Hongler (2018). Neural Tangent Kernel: Convergence and Generalization in Neural Networks (PDF). 32nd Conference on Neural Information Processing...

(Not to be confused with the lazy learning regime, see Neural tangent kernel). In machine learning, lazy learning is a learning method in which generalization...

Feedforward refers to recognition-inference architecture of neural networks. Artificial neural network architectures are based on inputs multiplied by weights...

In artificial neural networks, recurrent neural networks (RNNs) are designed for processing sequential data, such as text, speech, and time series, where...

')=e^{-\alpha \psi (\theta ,\theta ')},\alpha >0.} The sigmoid kernel, or hyperbolic tangent kernel, is defined as K ( x , y ) = tanh ⁡ ( γ x T y + r ) , x ...

Sohl-Dickstein, Jascha; Schoenholz, Samuel S. (2020). "Neural Tangents: Fast and Easy Infinite Neural Networks in Python". International Conference on Learning...

Anouar, F. (2000). "Generalized Discriminant Analysis Using a Kernel Approach". Neural Computation. 12 (10): 2385–2404. CiteSeerX 10.1.1.412.760. doi:10...

using the kernel trick, representing the data only through a set of pairwise similarity comparisons between the original data points using a kernel function...

initialized. Similarly, trainable parameters in convolutional neural networks (CNNs) are called kernels and biases, and this article also describes these. We...

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

model Kernel adaptive filter Kernel density estimation Kernel eigenvoice Kernel embedding of distributions Kernel method Kernel perceptron Kernel random...

Müller, K.-R. (1998). "Nonlinear Component Analysis as a Kernel Eigenvalue Problem". Neural Computation. 10 (5). MIT Press: 1299–1319. doi:10.1162/089976698300017467...

Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. The GRU is like a long short-term...

learning, a multilayer perceptron (MLP) is a name for a modern feedforward neural network consisting of fully connected neurons with nonlinear activation...

Long short-term memory (LSTM) is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem commonly encountered by traditional...

kernels of the previous neural network layer while i {\displaystyle i} iterates through the number of kernels of the current layer. In quantum neural...

implemented as a neural network, neural ODE methods would be needed. Indeed, CNF was first proposed in the same paper that proposed neural ODE. There are...

The Tangent loss is quasi-convex and is bounded for large negative values which makes it less sensitive to outliers. Interestingly, the Tangent loss...

and later layers encountered when training neural networks with backpropagation. In such methods, neural network weights are updated proportional to...

1988, LeCun et al. published a neural network design that recognize handwritten zip code. However, its convolutional kernels were hand-designed. In 1989...

1 {\displaystyle \sup _{x}|h'(x)|\leq 1} . For example, the hyperbolic tangent function h = tanh {\displaystyle h=\tanh } satisfies the requirement. Then...

v , w {\displaystyle v,w} on M {\displaystyle M} (i.e. sections of the tangent bundle T M {\displaystyle \mathrm {T} M} ), g ( v , w ) = g ′ ( f ∗ v ...

subalgebra of kernels which can be solved in O ( n ) {\displaystyle O(n)} . neural-tangents is a specialized package for infinitely wide neural networks....

dynamic reposing and tangent distance for drug activity prediction Archived 7 December 2019 at the Wayback Machine." Advances in Neural Information Processing...