Attention is a machine learning method that determines the importance of each component in a sequence relative to the other components in that sequence...

transformer is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism, which was proposed...

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn...

Self-attention can mean: Attention (machine learning), a machine learning technique self-attention, an attribute of natural cognition This disambiguation...

Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning...

"Attention Is All You Need" is a 2017 landmark research paper in machine learning authored by eight scientists working at Google. The paper introduced...

page is a timeline of machine learning. Major discoveries, achievements, milestones and other major events in machine learning are included. History of...

translation conference Workshop on Statistical Machine Translation. Gehring et al. combined a CNN encoder with an attention mechanism in 2017, which handled long-range...

neural networks used for attention (machine learning) This disambiguation page lists articles associated with the title Attention network. If an internal...

with wide support for machine learning algorithms, written in C and Lua. Attention (machine learning) Convolution Deep learning Natural-language processing...

interests and dislikes Attention (machine learning), a machine learning technique that mimics natural attention in organisms Attention (advertising), the...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

Mixture of experts (MoE) is a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous...

semantically more substantial regions in space, visual temporal attention modules enable machine learning algorithms to emphasize more on critical video frames...

A large language model (LLM) is a machine learning model designed for natural language processing tasks, especially language generation. LLMs are language...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

exaFLOPs. Transformer (machine learning model) Convolutional neural network Attention (machine learning) Perceiver Deep learning PyTorch TensorFlow Dosovitskiy...

In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable...

Seq2seq is a family of machine learning approaches used for natural language processing. Applications include language translation, image captioning, conversational...

develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize...

Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images...

(Cochrane Developmental, Psychosocial and Learning Problems Group) (August 2018). "Amphetamines for attention deficit hyperactivity disorder (ADHD) in...

Deep learning is a subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression...

In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization...

a direct result of the Learning by Observing and Pitching In model. Keen attention is both a requirement and result of learning by observing and pitching-in...

alternatives to rote learning include meaningful learning, associative learning, spaced repetition and active learning. Rote learning is widely used in the...

speech processing[citation needed]. Language modeling Transformer (machine learning model) State-space model Recurrent neural network The name comes from...

field of deep learning, most notably the development of the Transformer neural network, which he co-authored in landmark paper Attention Is All You Need...

AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), is a field of research within artificial intelligence (AI) that...

Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by biological neural...