In network science, a gradient network is a directed subnetwork of an undirected "substrate" network where each node has an associated scalar potential...

gradient problem is the problem of greatly diverging gradient magnitudes between earlier and later layers encountered when training neural networks with...

Broadcast communication network Butterfly network Computer network diagram Gradient network Internet topology Network simulation Relay network Rhizome (philosophy)...

machine learning, backpropagation is a gradient computation method commonly used for training a neural network to compute its parameter updates. It is...

extension of gradient descent, stochastic gradient descent, serves as the most basic algorithm used for training most deep networks today. Gradient descent...

Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals instead of residuals as...

providing a unifying view of gradient calculation techniques for recurrent networks with local feedback. One approach to gradient information computation in...

values in a given dataset. Gradient-based methods such as backpropagation are usually used to estimate the parameters of the network. During the training phase...

intelligent agent. Specifically, it is a policy gradient method, often used for deep RL when the policy network is very large. The predecessor to PPO, Trust...

conjugate gradient (Fletcher–Reeves update, Polak–Ribiére update, Powell–Beale restart, scaled conjugate gradient). Let N {\displaystyle N} be a network with...

initialized network, only about 50% of hidden units are activated (i.e. have a non-zero output). Better gradient propagation: fewer vanishing gradient problems...

Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e...

Shun'ichi Amari reported the first multilayered neural network trained by stochastic gradient descent, which was able to classify non-linearily separable...

theory Gradient network Higher category theory Immune network theory Irregular warfare Network analyzer Network dynamics Network formation Network theory...

discovered the vanishing gradient problem in 1991 and argued that it explained why the then-prevalent forms of recurrent neural networks did not work for long...

performance. In very deep networks, batch normalization can initially cause a severe gradient explosion—where updates to the network grow uncontrollably large—but...

training the wide neural network and kernel methods: gradient descent in the infinite-width limit is fully equivalent to kernel gradient descent with the NTK...

speed of convergence, the scale of neural activation within the network, the scale of gradient signals during backpropagation, and the quality of the final...

gradient method (Frank–Wolfe) for approximate minimization of specially structured problems with linear constraints, especially with traffic networks...

as the transformer. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by the regularization...

Williams, Ronald J. (1987). "A class of gradient-estimating algorithms for reinforcement learning in neural networks". Proceedings of the IEEE First International...

high-dimensional space of all possible neural network functions. The standard strategy of using gradient descent to find the equilibrium often does not...

In neural networks, the gating mechanism is an architectural motif for controlling the flow of activation and gradient signals. They are most prominently...

five stations of the network. The logo was simplified in 2003, effectively becoming simply two angled trapezoids, losing its gradient, shadows and colour-coded...

network Dynamic network analysis Dynamic single-frequency networks Gaussian network model Gene regulatory network Gradient network Network planning and design...

by the gradient of the function at the current point. Examples of gradient methods are the gradient descent and the conjugate gradient. Gradient descent...

through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks, such as Elman networks. The algorithm was independently...

with the highest IoU with the ground truth bounding boxes is used for gradient descent. Concretely, let j {\displaystyle j} be that predicted bounding...

Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. One prominent example is molecular...

the vanishing gradient problem. As long as the forget gates of the 2000 LSTM are open, it behaves like the 1997 LSTM. The Highway Network of May 2015 applies...