Applying machine learning (ML) (including deep learning) methods to the study of quantum systems is an emergent area of physics research. A basic example...

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

Quantum machine learning (QML) is the study of quantum algorithms which solve machine learning tasks. The most common use of the term refers to quantum...

Dynamical Systems in Training Physics-Informed Neural Networks". Transactions on Machine Learning Research. ISSN 2835-8856. Physics Informed Neural Network...

Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source)...

common feeling for better protection of machine learning systems in industrial applications. Machine learning techniques are mostly designed to work on...

capability. Beyond machine learning, the principles of feature engineering are applied in various scientific fields, including physics. For example, physicists...

In machine learning (ML), boosting is an ensemble learning method that combines a set of less accurate models (called "weak learners") to create a single...

Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination...

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

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating...

In machine learning, deep learning focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation...

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

In deep learning, transformer is an architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called...

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...

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

explainable AI (XAI), often overlapping with interpretable AI or explainable machine learning (XML), is a field of research that explores methods that provide humans...

Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related...

In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update...

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

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...

In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...

online machine learning There are many reasons for wanting to distribute intelligence or cope with multi-agent systems. Mainstream problems in DAI research...

statistical physics technique applied in the context of cognitive science. It is also classified as a Markov random field. Boltzmann machines are theoretically...

Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a...

In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and...

Logic learning machine (LLM) is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching...

In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves...

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...

used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning...