Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning...

order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised...

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

data and generalise to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances in the field...

parse tree or a labeled graph, then standard methods must be extended. Learning to rank: When the input is a set of objects and the desired output is a ranking...

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

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...

transformers), reinforcement learning, audio, multimodal learning, robotics, and even playing chess. It has also led to the development of pre-trained...

machine learning and artificial intelligence research. It is supported by the International Machine Learning Society (IMLS). Precise dates vary year to year...

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...

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...

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

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

deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University to address...

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)...

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

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

techniques. This normalised form distance is often used within many deep learning algorithms. In biology, there is a similar concept known as the Otsuka–Ochiai...

the learning rate is often varied during training either in accordance to a learning rate schedule or by using an adaptive learning rate. The learning rate...

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

Greg Hullender. 2005. Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning (ICML '05). ACM, New...

Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate...

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

metric called Mahalanobis distance. Similarity learning is used in information retrieval for learning to rank, in face verification or face identification...

In machine learning (ML), boosting is an ensemble metaheuristic for primarily reducing bias (as opposed to variance). It can also improve the stability...

Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"...

Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled...

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

rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify...