Search results
Results from the WOW.Com Content Network
Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some ...
A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models. While individual neurons are simple, many of them together in a network can perform complex tasks. There are two main types of neural network.
Graph attention network is a combination of a GNN and an attention layer. The implementation of attention layer in graphical neural networks helps provide attention or focus to the important information from the data instead of focusing on the whole data. A multi-head GAT layer can be expressed as follows:
In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [1] [2] A neural network consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain ...
This popularizes deep neural networks. [52] 2013 Discovery Word Embeddings A widely cited paper nicknamed word2vec revolutionizes the processing of text in machine learnings. It shows how each word can be converted into a sequence of numbers (word embeddings), the use of these vectors revolutionized text processing in machine learning. 2014 ...
The word problem for free monoidal categories, i.e. deciding whether two given diagrams are equal, can be solved in polynomial time. The interchanger is a confluent rewriting system on the subset of boundary connected diagrams, i.e. whenever the plane graphs have no more than one connected component which is not connected to the domain or ...
[19] [20] [21] A slow neural network learns by gradient descent to generate keys and values for computing the weight changes of the fast neural network which computes answers to queries. [17] This was later shown to be equivalent to the unnormalized linear Transformer. [22] A follow-up paper developed a similar system with active weight ...
In quantum neural networks programmed on gate-model quantum computers, based on quantum perceptrons instead of variational quantum circuits, the non-linearity of the activation function can be implemented with no need of measuring the output of each perceptron at each layer.