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Aspect of metacognition
Meta-learning is a branch of metacognition concerned with learning about one's own learning and learning processes. The term comes from the meta prefix's
Meta-learning
Subfield of machine learning
Meta-learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017
Meta-learning (computer science)
Meta-learning_(computer_science)
Artificial intelligence division of Meta Platforms
Meta AI is a research division of Meta (formerly Facebook) that develops artificial intelligence and augmented reality technologies. Meta AI was founded
Meta_AI
Process of automating the application of machine learning
include hyperparameter optimization, meta-learning and neural architecture search. In a typical machine learning application, practitioners have a set
Automated_machine_learning
Academic conference in machine learning
International Conference on Machine Learning (ICML) is an international academic conference in machine learning held annually since 1980. It is the oldest
International Conference on Machine Learning
International_Conference_on_Machine_Learning
Artificial intelligence division of Meta Platforms
Meta Superintelligence Labs (MSL) is an American artificial intelligence division of Meta Platforms, headquartered in Menlo Park, California. The division
Meta_Superintelligence_Labs
Subset of artificial intelligence
learning is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement learning, and finally meta-learning
Machine_learning
Machine learning-powered structure design
hyperparameter optimization and meta-learning and is a subfield of automated machine learning (AutoML). Reinforcement learning (RL) can underpin a NAS search
Neural_architecture_search
Algorithm for modelling sequential data
In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which text is
Transformer_(deep_learning)
Smartglasses
In 2023, Meta and Ray-Ban released Ray-Ban Meta, the second generation of the companies' smart-glasses line. Compared with Ray-Ban Stories, the model increased
Ray-Ban_Meta
Deep learning architecture
Mamba is a deep learning architecture focused on sequence modeling. It was developed by two researchers Albert Gu from Carnegie Mellon University and Tri
Mamba (deep learning architecture)
Mamba_(deep_learning_architecture)
Machine learning strategy
compares 'meta-learning approaches to active learning' to 'traditional heuristic-based Active Learning' may give intuitions if 'Learning active learning' is
Active learning (machine learning)
Active_learning_(machine_learning)
Overview of and topical guide to machine learning
Generalization Meta-learning Inductive bias Metadata Reinforcement learning Q-learning State–action–reward–state–action (SARSA) Temporal difference learning (TD)
Outline_of_machine_learning
Field of machine learning
Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. While supervised learning and
Reinforcement_learning
Type of artificial neural network
to exploit gradient-based meta-learning. In this case, the neural field is seen as the specialization of an underlying meta-neural-field, whose parameters
Neural_field
Type of large language model
generative artificial intelligence chatbots. GPTs are based on a deep learning architecture called the transformer. They are pre-trained on large datasets
Generative pre-trained transformer
Generative_pre-trained_transformer
Academic conference in machine learning
The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year.
International Conference on Learning Representations
International_Conference_on_Learning_Representations
German computer scientist (born 1963)
the 2010s. He also introduced principles of dynamic neural networks, meta-learning, generative adversarial networks and linear transformers, all of which
Jürgen_Schmidhuber
Machine learning methods using multiple input modalities
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
Multimodal_learning
Type of feedforward neural network
In deep learning, a multilayer perceptron (MLP) is a kind of modern feedforward neural network consisting of fully connected neurons with nonlinear activation
Multilayer_perceptron
Technique for the generative modeling of a continuous probability distribution
In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable
Diffusion_model
Type of machine learning model
26 September 2025. "Meta fends off authors' U.S. copyright lawsuit over AI". Reuters. 25 June 2025. Retrieved 26 June 2025. "Meta Scores Victory in AI
Large_language_model
Machine learning technique
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
Attention_(machine_learning)
Machine learning technique
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Machine learning paradigm using minimal training data
generalized from examples of other classes. Transfer learning Meta-learning (computer science) One-shot learning (computer vision) Wang, Yaqing; Yao, Quanming;
Few-shot_learning
American computer scientist and academic
to 'learn to learn', more akin to human learning than traditional machine learning systems. These “meta-learning” techniques train machines to quickly adapt
Chelsea_Finn
Largely debunked theories that aim to account for differences in individuals' learning
used learning styles as a method in the past year. It concluded that it might be better to use methods that are "demonstrably effective". A 2025 meta-analysis
Learning_styles
2018 text-generating language model
primarily employed supervised learning from large amounts of manually labeled data. This reliance on supervised learning limited their use of datasets
GPT-1
Computational model used in machine learning
In machine learning, a neural network (NN) or neural net, is a computational model inspired by the structure and functions of biological neural networks
Neural network (machine learning)
Neural_network_(machine_learning)
Large language model by Meta AI
Llama ("Large Language Model Meta AI" serving as a backronym) is a family of large language models (LLMs) released by Meta AI starting in February 2023
Llama_(language_model)
Structuring text as input to generative artificial intelligence
in-context learning is temporary. Training models to perform in-context learning can be viewed as a form of meta-learning, or "learning to learn". Research
Prompt_engineering
Model-free reinforcement learning algorithm
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
Q-learning
Paradigm in machine learning that uses no classification labels
machine learning Cluster analysis Model-based clustering Anomaly detection Expectation–maximization algorithm Generative topographic map Meta-learning (computer
Unsupervised_learning
Smooth approximation of one-hot arg max
term "softargmax", though the term "softmax" is conventional in machine learning. This section uses the term "softargmax" for clarity. Formally, instead
Softmax_function
Statistics and machine learning technique
some of the models that take a long time to train. Landmark learning is a meta-learning approach that seeks to solve this problem. It involves training
Ensemble_learning
Machine-learning and computational-neuroscience conference
Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference held annually in December. Along
Conference on Neural Information Processing Systems
Conference_on_Neural_Information_Processing_Systems
Mathematical folklore
justification of meta-learning: Is the no free lunch theorem a show-stopper." In Proceedings of the ICML-2005 Workshop on Meta-learning, pp. 12–19. 2005
No_free_lunch_theorem
Type of database that uses vectors to represent other data
from the raw data using machine learning methods such as feature extraction algorithms, word embeddings or deep learning networks. The goal is that semantically
Vector_database
Machine learning technique
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
Transfer_learning
Machine learning algorithm
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or
Decision_tree_learning
Educational strategy
Wikiversity has learning resources about Self-regulated learning Corrective feedback Educational psychology Learning by teaching Meta learning Reflective practice
Self-regulated_learning
Machine learning paradigm
Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals
Self-supervised_learning
2023 text-generating language model
reviews are used to fine-tune the system in a process called reinforcement learning from human feedback, which trains the model to refuse prompts which go
GPT-4
3D reconstruction technique
about half the size of ray-based NeRF. In 2021, researchers applied meta-learning to assign initial weights to the MLP. This rapidly speeds up convergence
Neural_radiance_field
Machine learning calibration technique
In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution
Platt_scaling
Concept in machine learning
In statistics and machine learning, leakage (also known as data leakage or target leakage) refers to the use of information during model training that
Leakage_(machine_learning)
German computer scientist
learning, particularly in the areas of automated machine learning (AutoML), hyperparameter optimization, meta-learning and tabular machine learning.
Frank_Hutter
Set of methods for supervised statistical learning
In machine learning, a support vector machine (SVM) or support vector network is a supervised max-margin model with associated learning algorithms that
Support_vector_machine
2023 text-generating language model
Foundation Models" (PDF). IBM. 2023-11-30. Fritts, Harold (2024-04-22). "IBM Adds Meta Llama 3 To watsonx, Expands AI Offerings". StorageReview.com. Retrieved 2024-05-08
IBM_Granite
Problem setup in machine learning
of Classes: A Meta-Learning Approach" (PDF). NeurIPS. Srivastava, Shashank; Labutov, Igor; Mitchelle, Tom (2018). "Zero-shot Learning of Classifiers
Zero-shot_learning
Reverse-engineering neural networks
identify structures, circuits or algorithms encoded in the weights of machine learning models. This contrasts with earlier interpretability methods that focused
Mechanistic_interpretability
Class of artificial neural network
whose middle layer contains recurrent connections that change by a Hebbian learning rule. Later, in Principles of Neurodynamics (1961), he described "closed-loop
Recurrent_neural_network
2025 multimodal model by OpenAI
stages: unsupervised pretraining, supervised fine-tuning, and reinforcement learning from human feedback. Pretraining used a large-scale multilingual dataset
GPT-5
Framework for mathematical analysis of machine learning
computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed
Probably approximately correct learning
Probably_approximately_correct_learning
Similarity measure for number sequences
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
Cosine_similarity
Memory unit used in neural networks
Bahdanau, Dzmitry; Bougares, Fethi; Schwenk, Holger; Bengio, Yoshua (2014). "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine
Gated_recurrent_unit
Automated recognition of patterns and regularities in data
retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some
Pattern_recognition
Tree-based ensemble machine learning methods
Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude
Random_forest
Algorithm for supervised learning of binary classifiers
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether
Perceptron
Mixed/virtual reality headset
Meta Quest 3 is a standalone virtual reality (VR) headset developed by Reality Labs, a division of Meta Platforms. It was unveiled on June 1, 2023, and
Meta_Quest_3
Measurable property or characteristic
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating
Feature_(machine_learning)
Technique in machine learning
Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"
Curriculum_learning
Models used to produce word embeddings
Rong, Xin (5 June 2016), word2vec Parameter Learning Explained, arXiv:1411.2738 Hinton, Geoffrey E. "Learning distributed representations of concepts."
Word2vec
Conversational software
would behave as a conversational partner. Such chatbots often use deep learning and natural language processing. Simpler chatbots have existed for decades
Chatbot
Tuning parameter (hyperparameter) in optimization
In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration
Learning_rate
Hypothetical self-improving program
machine is often discussed when dealing with issues of meta-learning, also known as "learning to learn." Applications include automating human design
Gödel_machine
Method used to normalize the range of independent variables
Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work properly without normalization
Feature_scaling
Type of artificial neural network
these early efforts did not lead to a working learning algorithm for hidden units, i.e., deep learning. In 1965, Alexey Grigorevich Ivakhnenko and Valentin
Feedforward_neural_network
Self-awareness about thinking, higher-order thinking skills
Self-Regulated Learning in Educational Technology An Interdisciplinary perspective on expertise under the META framework. Includes meta-motivation, meta-emotion
Metacognition
Integrated circuit technology
digital, or mixed-mode VLSI, prioritize robustness, adaptability, and learning by emulating the brain’s distributed processing across small computing
Neuromorphic_computing
Method in natural language processing
meaning. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors
Word_embedding
Model-free reinforcement learning algorithm
Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient
Proximal_policy_optimization
Deep learning library
PyTorch is an open-source deep learning library, originally developed by Meta Platforms and currently developed with support from the Linux Foundation
PyTorch
French computer scientist (born 1960)
'Intelligence really is about learning'". ft.com. London: Financial Times. Retrieved 11 November 2025. "I'm sure there's a lot of people at Meta who would like me
Yann_LeCun
Optimization algorithm
become an important optimization method in machine learning. Both statistical estimation and machine learning consider the problem of minimizing an objective
Stochastic_gradient_descent
Framework for machine learning
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory
Statistical_learning_theory
Theory of machine learning
Theoretical results in machine learning often focus on a type of inductive learning known as supervised learning. In supervised learning, an algorithm is provided
Computational_learning_theory
Academic journal
The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning. It was established in 2000 and the
Journal of Machine Learning Research
Journal_of_Machine_Learning_Research
Flaw in mathematical modelling
overfitting occurs when a model begins to "memorize" training data rather than "learning" to generalize from a trend. As an extreme example, if the number of parameters
Overfitting
Machine learning software library
MindSpore is an open-source software framework for deep learning, machine learning and artificial intelligence developed by Huawei. MindSpore provides
MindSpore
Research field that lies at the intersection of machine learning and computer security
Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. Machine learning techniques
Adversarial_machine_learning
Use of machine learning to rank items
Learning to rank (LTR) or machine-learned ranking (MLR) is the application of machine learning, often supervised, semi-supervised or reinforcement learning
Learning_to_rank
Computer programming concept
search) Self Learning Meta-Tic-Tac-Toe Archived 2014-03-19 at the Wayback Machine Example web app showing how temporal difference learning can be used
Temporal_difference_learning
Research laboratory in Montreal, Canada
focused on deep learning and reinforcement learning. Specific research topics include: generative models natural language processing meta learning computer vision
Mila_(research_institute)
Machine learning technique
In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization
Normalization (machine learning)
Normalization_(machine_learning)
Optimization algorithm
methods for optimization. Gradient descent is particularly useful in machine learning and artificial intelligence for minimizing the cost or loss function. Gradient
Gradient_descent
Numerical method that reduces the complexity of computationally intensive simulations
model; to this end, the method is also associated with the field of machine learning. The main use of POD is to decompose a physical field (like pressure, temperature
Proper orthogonal decomposition
Proper_orthogonal_decomposition
Type of feedforward neural network
learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different
Convolutional_neural_network
Ensemble learning method
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
Boosting_(machine_learning)
Statistical method that summarizes and/or integrates data from multiple sources
Meta-analysis is a method of synthesis of quantitative data from multiple independent studies addressing a common research question. An important part
Meta-analysis
Type of convolutional neural network
regression using U-Net and its application on pansharpening; 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation; TernausNet: U-Net
U-Net
Machine learning technique
Mixture of experts (MoE) is a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous
Mixture_of_experts
American entrepreneur (born 1981)
Finn” (podcast, February 15, 2024). A pioneer in meta-learning and robotics, she detailed robots learning complex tasks like cooking. Andrew Feldman – Co-founder
Lukas_Biewald
2019 text-generating language model
exaggerated; Anima Anandkumar, a professor at Caltech and director of machine learning research at Nvidia, said that there was no evidence that GPT-2 had the
GPT-2
2018 video game
Micah McGonigal. Parodying 1990s educational games; it was made for the "Meta Game Jam" in 2018. The player is tasked to collect seven notebooks and escape
Baldi's Basics in Education and Learning
Baldi's_Basics_in_Education_and_Learning
Machine learning that combines deep learning and reinforcement learning
Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem
Deep_reinforcement_learning
Statistical model of language
October 2020. Retrieved 25 February 2019. "llama/MODEL_CARD.md at main · meta-llama/llama". GitHub. Retrieved 28 December 2024. Jay M. Ponte; W. Bruce
Language_model
Set of statistical processes for estimating the relationships among variables
(often called the outcome or response variable, or a label in machine learning parlance) and one or more independent variables (often called regressors
Regression_analysis
Recurrent neural network architecture
Hidden Markov Models. Hochreiter et al. used LSTM for meta-learning (i.e. learning a learning algorithm). 2004: First successful application of LSTM
Long_short-term_memory
Extracting features from raw data for machine learning
Feature engineering is a preprocessing step in supervised machine learning and statistical modeling which transforms raw data into a more effective set
Feature_engineering
META LEARNING
META LEARNING
Girl/Female
Hebrew American Spanish
Grace.
Girl/Female
Indian
Love
Female
Hebrew
(× Ö¶×˜Ö·×¢) Hebrew unisex name NETA means meaning "plant, shrub."
Female
Spanish
 Short form of Spanish Aleta, LETA means "winged." Compare with another form of Leta.
Girl/Female
Danish American Greek Persian Latin
Boy/Male
Muslim
Obeyed, Pure or like a Pearl
Female
English
English name derived from the second letter of the Greek alphabet, beta, related to Hebrew bet, BETA means "house."Â
Girl/Female
Hindu
Precious blue stone, Fish, Jewel (Wife of the himalayas)
Female
German
Short form of German Margarete, META means "pearl."
Female
Native American
 Native American Blackfoot name PETA means "golden eagle." Compare with another form of Peta.
Girl/Female
Greek American
Speaker.
Girl/Female
Hindu
A friend
Female
Italian
 Variant spelling of Italian Zita, ZETA means "little girl." Compare with another form of Zeta.
Girl/Female
Muslim
Girl/Female
Australian, Danish, Dutch, German, Greek, Latin, Slovenia, Swedish
Ambitious; Pearl
Girl/Female
Hindu
Cloud
Girl/Female
Greek Native American
Stone; rock.
Female
Hawaiian
Hawaiian name MEKA means "eyes."
Female
Hungarian
Hungarian feminine form of Latin Timæus, TÃMEA means "honor."
Girl/Female
Latin
Goddess of silence.
META LEARNING
META LEARNING
Male
Egyptian
, the spirit lord of Tattu.
Girl/Female
Tamil
Pratigya | பà¯à®°à®¤à®¿à®œà¯à®žà®¾
Pledge, Vow
Boy/Male
Hindu, Indian
Mountains
Girl/Female
Muslim/Islamic
Sky
Boy/Male
Indian, Sanskrit
Lord of Candi; Lord Shiva
Boy/Male
American, Australian, British, Christian, English, German, Latin
Woodsman; Lives in Wood; Wood-dweller; From the Wood
Boy/Male
Hindu
Boy/Male
Muslim
Appearance
Surname or Lastname
English and Scottish
English and Scottish : from a medieval variant of Marshall.
Girl/Female
Indian
Beautiful, Shining star, One and only
META LEARNING
META LEARNING
META LEARNING
META LEARNING
META LEARNING
n.
Measure; limit; boundary; -- used chiefly in the plural, and in the phrase metes and bounds.
n.
Any slender, more or less rigid, bristlelike organ or part; as the hairs of a caterpillar, the slender spines of a crustacean, the hairlike processes of a protozoan, the bristles or stiff hairs on the leaves of some plants, or the pedicel of the capsule of a moss.
n.
Minced meat; meat chopped very fine; a mixture of boiled meat, suet, apples, etc., chopped very fine, to which spices and raisins are added; -- used in making mince pie.
n.
Ore from which a metal is derived; -- so called by miners.
p. p.
of Mete
n.
The flesh of animals used as food; esp., animal muscle; as, a breakfast of bread and fruit without meat.
n.
One of the movable chitinous spines or hooks of an annelid. They usually arise in clusters from muscular capsules, and are used in locomotion and for defense. They are very diverse in form.
n.
See Meathe.
n.
One of the spinelike feathers at the base of the bill of certain birds.
n.
Food, in general; anything eaten for nourishment, either by man or beast. Hence, the edible part of anything; as, the meat of a lobster, a nut, or an egg.
n.
Meat.
n.
A Greek letter corresponding to our z.
v. t.
To cover with metal; as, to metal a ship's bottom; to metal a road.