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Machine learning paradigm
parameters. Next, the actual task is performed with supervised or unsupervised learning. Self-supervised learning has produced promising results in recent years
Self-supervised_learning
Paradigm in machine learning
Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the advent
Weak_supervision
Set of learning techniques in machine learning
explicit algorithms. Feature learning can be either supervised, unsupervised, or self-supervised: In supervised feature learning, features are learned using
Feature_learning
Computational model used in machine learning
parameters. Next, the actual task is performed with supervised or unsupervised learning. Self-supervised learning has produced promising results in recent years
Neural network (machine learning)
Neural_network_(machine_learning)
Paradigm in machine learning that uses no classification labels
Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled
Unsupervised_learning
Subset of artificial intelligence
perform a specific task. Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labelled
Machine_learning
Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. While the computational implementations of ANNs
History of artificial neural networks
History_of_artificial_neural_networks
Algorithm for modelling sequential data
Transformers typically are first pretrained by self-supervised learning on a large generic dataset, followed by supervised fine-tuning on a small task-specific
Transformer_(deep_learning)
Machine learning technique
attention maps. Because vision transformers are typically trained in a self-supervised manner, attention maps are generally not class-sensitive. When a classification
Attention_(machine_learning)
Machine learning technique
feedback, learning a reward model, and optimizing the policy. Compared to data collection for techniques like unsupervised or self-supervised learning, collecting
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Type of large language model
(GP) was a long-established technique in machine learning. GP is a form of self-supervised learning wherein a model is first trained on a large, unlabeled
Generative pre-trained transformer
Generative_pre-trained_transformer
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
are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. For the training cost column, 1 petaFLOP-day
List_of_large_language_models
Principle in artificial intelligence
Speech Decoding With Self-Supervised Learning". Forty-second International Conference on Machine Learning. Proceedings of Machine Learning Research. Retrieved
Bitter_lesson
Technique in machine learning
2024. "Curriculum learning with diversity for supervised computer vision tasks". Retrieved March 29, 2024. "Self-paced Curriculum Learning". Retrieved March
Curriculum_learning
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
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
Type of feedforward neural network
radial basis networks, another class of supervised neural network models). In recent developments of deep learning the rectified linear unit (ReLU) is more
Multilayer_perceptron
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 model for vision processing
(2023-04-14). "DINOv2: Learning Robust Visual Features without Supervision". arXiv:2304.07193 [cs.CV]. "DINOv3: Self-supervised learning for vision at unprecedented
Vision_transformer
2016 browser game by Google LLC
Reinforcement Learning for Games. Packt Publishing Ltd. (published January 2020). ISBN 978-1-83921-493-6. "What is Self-Supervised Learning? | Stanford
Quick,_Draw!
2018 text-generating language model
models primarily employed supervised learning from large amounts of manually labeled data. This reliance on supervised learning limited their use of datasets
GPT-1
Type of feedforward neural network
activation map use the same set of parameters that define the filter. Self-supervised learning has been adapted for use in convolutional layers by using sparse
Convolutional_neural_network
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
Method in natural language processing
(and multi-lingual) corpora, also providing an early example of self-supervised learning of word embeddings. Word embeddings come in two different styles
Word_embedding
Overview of and topical guide to machine learning
computing Application of statistics Supervised learning, where the model is trained on labeled data Unsupervised learning, where the model tries to identify
Outline_of_machine_learning
Phenomenon in machine learning
about how the data is spread out. It is frequently encountered in self-supervised learning, especially within contrastive and non-contrastive frameworks,
Representation_collapse
Machine learning technique where agents learn from demonstrations
Imitation learning is a paradigm in reinforcement learning, where an agent learns to perform a task by supervised learning from expert demonstrations
Imitation_learning
Concept in machine learning
invalidating the model) Data dredging Overfitting Resampling (statistics) Supervised learning Training, validation, and test sets Shachar Kaufman; Saharon Rosset;
Leakage_(machine_learning)
Machine learning calibration technique
Alexandru; Caruana, Rich (2005). Predicting good probabilities with supervised learning (PDF). ICML. doi:10.1145/1102351.1102430. Olivier Chapelle; Vladimir
Platt_scaling
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
Software for understanding biological data
neighbors are processed with convolutional filters. Unlike supervised methods, self-supervised learning methods learn representations without relying on annotated
Machine learning in bioinformatics
Machine_learning_in_bioinformatics
Framework for machine learning
perspective of statistical learning theory, supervised learning is best understood. Supervised learning involves learning from a training set of data
Statistical_learning_theory
Machine learning technique
next driver of machine learning commercial success after supervised learning. In the 2020 paper, "Rethinking Pre-Training and self-training", Zoph et al
Transfer_learning
Ensemble learning method
reducing bias. Boosting is a popular and effective technique used in supervised learning for both classification and regression tasks. The theoretical foundation
Boosting_(machine_learning)
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
Artificial intelligence division of Meta Platforms
formerly the CTO of IBM's big data group. FAIR's research includes self-supervised learning, generative adversarial networks, document classification and translation
Meta_AI
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)
Series of language models developed by Google AI
Google. It learns to represent text as a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture. BERT dramatically
BERT_(language_model)
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
Internal representation of world by AI
train with self-supervised learning. They use large unlabeled datasets of video or robot interactions. Self-supervised learning can speed learning. Reinforcement
World model (artificial intelligence)
World_model_(artificial_intelligence)
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
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
Machine learning that combines deep learning and reinforcement learning
an artificial neural network. Deep learning methods, often using supervised learning with labeled datasets, have been shown to solve tasks that involve
Deep_reinforcement_learning
Method of speech synthesis that uses deep neural networks
self-supervised learning has gained much attention through better use of unlabelled data. Research has shown that, with the aid of self-supervised loss
Deep learning speech synthesis
Deep_learning_speech_synthesis
2025 multimodal model by OpenAI
involved three stages: unsupervised pretraining, supervised fine-tuning, and reinforcement learning from human feedback. Pretraining used a large-scale
GPT-5
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
2023 text-generating language model
was trained using a combination of first supervised learning on a large dataset, then reinforcement learning using both human and AI feedback, it did
GPT-4
Process of automating the application of machine learning
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination
Automated_machine_learning
Set of methods for supervised statistical learning
In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms
Support_vector_machine
Supervised machine learning techniques
Structured prediction or structured output learning is an umbrella term for supervised machine learning techniques that involves predicting structured
Structured_prediction
Type of machine learning model
like reinforcement learning from human feedback (RLHF) or constitutional AI. Instruction fine-tuning is a form of supervised learning used to teach LLMs
Large_language_model
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
German artificial intelligence company
architecture of generative pre-trained transformers (GPT) and self-supervised learning. Its method makes patterns learned by GPT models visible and controllable
Aleph_Alpha
Type of artificial neural network
radial basis networks, another class of supervised neural network models). In recent developments of deep learning, the rectified linear unit (ReLU) is more
Feedforward_neural_network
Subfield of machine learning
Conwell built a successful supervised meta-learner based on Long short-term memory RNNs. It learned through backpropagation a learning algorithm for quadratic
Meta-learning (computer science)
Meta-learning_(computer_science)
Machine learning technique useful for dimensionality reduction
A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically
Self-organizing_map
Class of artificial neural networks
passing" for such approaches. In the more general subject of "geometric deep learning", certain existing neural network architectures can be interpreted as GNNs
Graph_neural_network
Voice conversion software
reconstruction errors. Research on RVC has recently explored the use of self-supervised learning (SSL) encoders such as wav2vec 2.0 and HuBERT to replace hand-engineered
Retrieval-based Voice Conversion
Retrieval-based_Voice_Conversion
Integrated circuit technology
for efficient learning and inference. Also in 2017 IMEC’s self-learning chip, based on OxRAM, demonstrated music composition by learning from minuets.
Neuromorphic_computing
Technique for the generative modeling of a continuous probability distribution
perspective for supervised inverse problems. For example, Inversion by Direct Iteration (InDI) formulates image restoration by learning a residual flow
Diffusion_model
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
Automated recognition of patterns and regularities in data
categorized according to the type of learning procedure used to generate the output value. Supervised learning assumes that a set of training data (the
Pattern_recognition
Large language model by Meta AI
Llama 1 models are only available as foundational models with self-supervised learning and without fine-tuning. Llama 2 – Chat models were derived from
Llama_(language_model)
2020 text-generating language model
transformer-based deep-learning neural network architectures. Previously, the best-performing neural NLP models commonly employed supervised learning from large amounts
GPT-3
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
Voice clips generated by AI
Zhenyu; Mian, Li; Wang, Zhaoyu; Zhang, Jing; Tang, Jie (2021). "Self-supervised Learning: Generative or Contrastive". IEEE Transactions on Knowledge and
Audio_deepfake
Class of artificial neural network
predictability in the incoming data sequence, the highest level RNN can use supervised learning to easily classify even deep sequences with long intervals between
Recurrent_neural_network
Reverse-engineering neural networks
Neel (2023). "Emergent Linear Representations in World Models of Self-Supervised Sequence Models". BlackNLP Workshop: 16–30. doi:10.18653/v1/2023.blackboxnlp-1
Mechanistic_interpretability
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
Topics referred to by the same term
standard Solid-state lighting Semi-supervised learning and Self-supervised learning, classes of machine learning techniques Single stuck line, a fault
SSL
Type of activation function
performance without unsupervised pre-training, especially on large, purely supervised tasks. In 2017, the rectified linear function became a central component
Rectified_linear_unit
Plot of machine learning model performance over time or experience
descent "Mohr, Felix and van Rijn, Jan N. "Learning Curves for Decision Making in Supervised Machine Learning - A Survey." arXiv preprint arXiv:2201.12150
Learning curve (machine learning)
Learning_curve_(machine_learning)
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
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
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
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
Computer programming concept
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate
Temporal_difference_learning
Tuning parameter (hyperparameter) in optimization
Overfitting Backpropagation AutoML Model selection Self-tuning Murphy, Kevin P. (2012). Machine Learning: A Probabilistic Perspective. Cambridge: MIT Press
Learning_rate
Statistics and machine learning technique
much more flexible structure to exist among those alternatives. Supervised learning algorithms search through a hypothesis space to find a suitable hypothesis
Ensemble_learning
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
American computer scientist
of self-supervised learning to autonomous driving with the benefit of avoiding human intervention. His dissertation states that this self-supervised learning
David_Stavens
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)
Method in machine learning
called bagging (from bootstrap aggregating) or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and accuracy
Bootstrap_aggregating
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
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
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
Method of machine learning
online learning paradigms for LLMs to enable continuous, real-time adaptation after the initial training. In the setting of supervised learning, a function
Online_machine_learning
Deep learning generative model to encode data representation
designed for unsupervised learning, its effectiveness has been proven for semi-supervised learning and supervised learning. A variational autoencoder
Variational_autoencoder
Tasks in machine learning
naive Bayes classifier) is trained on the training data set using a supervised learning method, for example using optimization methods such as gradient descent
Training, validation, and test data sets
Training,_validation,_and_test_data_sets
Method of machine learning
train the model. It represents a dynamic technique of supervised learning and unsupervised learning that can be applied when training data becomes available
Incremental_learning
Branch of machine learning
thousands) in the network. Methods used can be supervised, semi-supervised or unsupervised. Some common deep learning network architectures include fully connected
Deep_learning
Programming paradigm
parameters in the program, often via gradient descent, as well as other learning approaches that are based on higher-order derivative information. Differentiable
Differentiable_programming
2023 text-generating language model
learning and data mining Paradigms Supervised learning Unsupervised learning Semi-supervised learning Self-supervised learning Reinforcement learning
IBM_Granite
AI's tendency to abruptly and drastically forget old info after learning new info
artificial neural architecture with memory self-refreshing that overcomes catastrophic interference when sequential learning tasks are carried out in distributed
Catastrophic_interference
Reinforcement learning technique
Self-play is a technique for improving the performance of reinforcement learning agents. Intuitively, agents learn to improve their performance by playing
Self-play
Machine learning strategy
concept can often be much lower than the number required in normal supervised learning. However, there is a risk that the algorithm is overwhelmed by uninformative
Active learning (machine learning)
Active_learning_(machine_learning)
Machine learning model training problem
trained further by supervised backpropagation to classify labeled data. The deep belief network model by Hinton et al. (2006) involves learning the distribution
Vanishing_gradient_problem
Neural network that learns efficient data encoding in an unsupervised manner
The fundamental challenge which comes with the unsupervised (self-supervised) learning setting is, that labels for rare events do not exist (in which
Autoencoder
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
SELF SUPERVISED-LEARNING
SELF SUPERVISED-LEARNING
Boy/Male
Welsh
peace'.
Male
English
(סֶלַע) Anglicized form of Hebrew Cela, SELA means "a rock." In the Old Testament bible, this is the name of the capital city of Edom, possibly an early name for Petra. In use as a unisex name.
Boy/Male
Muslim/Islamic
Sword
Male
Yiddish
(סֶעף) Variant spelling of Yiddish Zeff, SEFF means "wolf."
Surname or Lastname
English
English : from Middle English selle, a rough hut of the type normally occupied by animals, hence a topographic name for someone who lived in a hut like this. In many cases the name may have been in effect a metonymic occupational name for a herdsman.Americanized spelling of Hungarian and Hungarian Jewish Széll, a topographic name for someone who lived in a spot exposed to the wind, from Hungarian szél ‘wind’.German : variant of Selle.
Boy/Male
African, Arabic, Hindu, Indian, Muslim, Sindhi, Swahili
Sword; Brave; Sword of Religion
Girl/Female
Egyptian
Female
Egyptian
, a form of Isis.
Boy/Male
Indian
Sword
Boy/Male
Hindi
Self.
Male
Welsh
Welsh form of Greek SolomÅn, SELYF means "peaceable."Â
Boy/Male
Muslim
Sword
Boy/Male
British, English, Nigerian, Norwegian
Rock
Girl/Female
African, Australian, British, Chinese, Christian, English, French, Greek, Hawaiian, Hebrew
Saviour; Ewe of West Africa; Goddess of the Moon; Cliff; Rock
Surname or Lastname
English (East Anglia)
English (East Anglia) : from the Middle English personal name Saulf, Old English Sǣwulf, composed of the elements sǣ ‘sea’ + wulf ‘wolf’.
Biblical
a rock
Boy/Male
British, English, Hebrew
A Tree
Boy/Male
Biblical
A rock.
Girl/Female
British, English
Soft
Girl/Female
Hebrew Biblical
Rock.
SELF SUPERVISED-LEARNING
SELF SUPERVISED-LEARNING
Boy/Male
Tamil
Life, Soul
Girl/Female
Australian, Christian, Danish, German, Hebrew, Swedish
Bitter Grace; God is Gracious; God has Shown Favor
Boy/Male
Tamil
Lord Krishna
Male
Egyptian
, the keeper of the royal house of the women of Seti I.
Surname or Lastname
English (east midlands)
English (east midlands) : habitational name from Fritchley in Derbyshire.
Girl/Female
Hindu
Descendent, Daughter
Girl/Female
Hindu, Indian
Cute
Boy/Male
Muslim/Islamic
Champion
Male
English
From the surname which was derived from Middle English Aldrich, ALDRIDGE means "noble ruler."
Girl/Female
Arabic, Muslim, Persian
Happy; Pleased
SELF SUPERVISED-LEARNING
SELF SUPERVISED-LEARNING
SELF SUPERVISED-LEARNING
SELF SUPERVISED-LEARNING
SELF SUPERVISED-LEARNING
n.
One who supervises; an overseer; an inspector; a superintendent; as, a supervisor of schools.
a.
Refusing to gratify one's self; self-sacrificing.
n.
Faith in one's self; self-reliance.
n.
Self-communion.
n.
Self.
n.
Self-devotion.
n.
Control of one's self; restraint exercised over one's self; self-command.
n.
Self-denial; self-renunciation; self-sacrifice.
n.
Self-love.
v. t.
To oversee for direction; to superintend; to inspect with authority; as, to supervise the construction of a steam engine, or the printing of a book.
n.
Restraint over one's self; self-control; self-command.
p. pr. & vb. n.
of Supervise
n.
Self-deceit.
imp. & p. p.
of Supervise
a.
Self-repelling.
n.
The act of governing one's self, or the state of being governed by one's self; self-control; self-command.
a.
Dependent on one's self; self-depending; self-reliant.
n.
Enjoyment of one's self; self-satisfaction.
a.
Disposed to self-assertion; self-asserting.
n.
Imposture practiced on one's self; self-deceit.