artificial intelligence, apprenticeship learning (or learning from demonstration or imitation learning) is the process of learning by observing an expert...

Active learning (machine learning) Apprenticeship learning Error-driven learning Model-free (reinforcement learning) Multi-agent reinforcement learning Optimal...

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

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

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

Apprenticeship is a system for training a potential new practitioners of a trade or profession with on-the-job training and often some accompanying study...

International Conference on Machine Learning (ICML) is a leading international academic conference in machine learning. Along with NeurIPS and ICLR, it is...

from expert demonstrations. It is also called learning from demonstration and apprenticeship learning. It has been applied to underactuated robotics...

Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or...

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

Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring...

Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude...

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

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

apprentice perspective is an educational theory of apprenticeship concerning the process of learning through active participation in the practices of the...

The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year....

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether...

whose middle layer contains recurrent connections that change by a Hebbian learning rule.: 73–75  Later, in Principles of Neurodynamics (1961), he described...

Mamba is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University...

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

token. After this step, the model was then fine-tuned with reinforcement learning feedback from humans and AI for human alignment and policy compliance.: 2 ...

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

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

term "softargmax", though the term "softmax" is conventional in machine learning. This section uses the term "softargmax" for clarity. Formally, instead...

learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different...

In machine learning (ML), feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations...

natural language processing by machines. It is based on the transformer deep learning architecture, pre-trained on large data sets of unlabeled text, and able...

primarily employed supervised learning from large amounts of manually labeled data. This reliance on supervised learning limited their use of datasets...

Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work properly without normalization...