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Machine learning paradigm
In machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based
Supervised_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
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
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
Computational model used in machine learning
Zhai X, Oliver A, Kolesnikov A (October 2019). "S4L: Self-Supervised Semi-Supervised Learning". 2019 IEEE/CVF International Conference on Computer Vision
Neural network (machine learning)
Neural_network_(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
Algorithm for modelling sequential data
typically are first pretrained by self-supervised learning on a large generic dataset, followed by supervised fine-tuning on a small task-specific dataset
Transformer_(deep_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
Use of artificial intelligence in the automation of electronic design
include supervised learning, unsupervised learning, reinforcement learning, and generative AI. Supervised learning is a type of machine learning where algorithms
AI-driven_design_automation
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
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
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
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
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
Paradigm of rule-based machine learning methods
computation) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). Learning classifier systems
Learning_classifier_system
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
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
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
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
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
Machine learning technique
that TL would become the next driver of machine learning commercial success after supervised learning. In the 2020 paper, "Rethinking Pre-Training and
Transfer_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
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
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
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
Branch of computer science
design and analysis. AIARE encompasses several AI methodologies: Supervised learning employs tagged data to train models to recognize system components
AI-assisted reverse engineering
AI-assisted_reverse_engineering
Artificial neural network that mimics neurons
unsupervised biologically inspired learning methods are available such as Hebbian learning and STDP, no effective supervised training method is suitable for
Spiking_neural_network
Type of feedforward neural network
visual scenes even when the objects are shifted. Several supervised and unsupervised learning algorithms have been proposed over the decades to train the
Convolutional_neural_network
Principle in artificial intelligence
Decoding With Self-Supervised Learning". Forty-second International Conference on Machine Learning. Proceedings of Machine Learning Research. Retrieved
Bitter_lesson
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)
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
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
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
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
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
Branch of biology
are gene regulatory, protein interaction and metabolic networks. Supervised learning is a type of algorithm that learns from labeled data and learns how
Computational_biology
Deep learning method
unsupervised learning, GANs have also proved useful for semi-supervised learning, fully supervised learning, and reinforcement learning. The core idea
Generative adversarial network
Generative_adversarial_network
Technique in machine learning
"CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images". arXiv:1808.01097 [cs.CV]. "Competence-based curriculum learning for neural machine
Curriculum_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
Technique to make a model more generalizable and transferable
gather than input examples, semi-supervised learning can be useful. Regularizers have been designed to guide learning algorithms to learn models that respect
Regularization_(mathematics)
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
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
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)
Computer-based method for summarizing a text
text about machine learning, the unigram "learning" might co-occur with "machine", "supervised", "un-supervised", and "semi-supervised" in four different
Automatic_summarization
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
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
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)
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
Data analysis techniques for fraud detection
The machine learning and artificial intelligence solutions may be classified into two categories: 'supervised' and 'unsupervised' learning. These methods
Data analysis for fraud detection
Data_analysis_for_fraud_detection
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
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
Concept in artificial intelligence
of learning by observing an expert. It can be viewed as a form of supervised learning, where the training dataset consists of task executions by a demonstration
Apprenticeship_learning
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)
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
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
Machine learning technique
typically accomplished via supervised learning, but there are also techniques to fine-tune a model using weak supervision. Fine-tuning can be combined
Fine-tuning_(deep_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
Intelligence of machines
machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance. Supervised learning requires
Artificial_intelligence
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
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
Type of statistical inference
In logic, statistical inference, and supervised learning, transduction or transductive inference is reasoning from observed, specific (training) cases
Transduction (machine learning)
Transduction_(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
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
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
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)
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
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
Framework for machine learning
prediction. Learning falls into many categories, including supervised learning, unsupervised learning, online learning, and reinforcement learning. From the
Statistical_learning_theory
Method in natural language processing
multi-lingual) corpora, also providing an early example of self-supervised learning of word embeddings. Word embeddings come in two different styles
Word_embedding
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 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
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
Overview of and topical guide to deep learning
language model Supervised learning Unsupervised learning Self-supervised learning Semi-supervised learning Reinforcement learning Transfer learning Multitask
Outline_of_deep_learning
datasets. High-quality labeled training datasets for supervised and semi-supervised machine-learning algorithms are usually difficult and expensive to produce
List of datasets for machine-learning research
List_of_datasets_for_machine-learning_research
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
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
Vector quantization algorithm minimizing the sum of squared deviations
relationship to the k-nearest neighbor classifier, a popular supervised machine learning technique for classification that is often confused with k-means
K-means_clustering
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
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)
Type of artificial intelligence system
models (LLMs), which are limited to text. It is an example of multimodal learning. Many widely used commercial applications now rely on this ability. OpenAI
Vision–language_model
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
Internal representation of world by AI
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)
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
Identification of which sense of a word is being used
machine learning. Many techniques have been researched, including dictionary-based methods that use the knowledge encoded in lexical resources, supervised machine
Word-sense_disambiguation
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
Property of a model
prevent supervised learning algorithms from generalizing beyond their training set: The bias error is an error from erroneous assumptions in the learning algorithm
Bias–variance_tradeoff
Process of analyzing large data sets
in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary
Data_mining
Supervised learning of a similarity function
Similarity learning is an area of supervised machine learning in artificial intelligence. It is closely related to regression and classification, but the
Similarity_learning
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
Optimization algorithm for artificial neural networks
case of reverse accumulation (or "reverse mode"). The goal of any supervised learning algorithm is to find a function that best maps a set of inputs to
Backpropagation
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
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
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
Difficulties arising when analyzing data with many aspects ("dimensions")
techniques for classification (including the k-NN classifier), semi-supervised learning, and clustering, and it also affects information retrieval. In a
Curse_of_dimensionality
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
Machine learning software library
TensorFlow is a software library for machine learning and artificial intelligence. It can be used across a range of tasks, but is used mainly for training
TensorFlow
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
SUPERVISED LEARNING
SUPERVISED LEARNING
Boy/Male
Muslim/Islamic
Observer supervisor
Girl/Female
Muslim
Warner, Observer, Supervisor
Boy/Male
Indian, Punjabi, Sikh
Supervisor; Eye Sight
Boy/Male
Muslim
One who warns, Bright, Radiant, Blooming, Observer, Supervisor
Girl/Female
Arabic, Muslim
Like; Equal; Matching; Observer; Supervisor
Girl/Female
Muslim
Like, Equal, Matching, Observer, Supervisor
Boy/Male
American, Anglo, Arabic, Australian, British, Chinese, Christian, Danish, English, French, German, Greek, Jamaican, Latin, Muslim
Hollow; Valley; Church Official; Supervisor
Girl/Female
Muslim
Warner, Observer, Supervisor
Girl/Female
American, Anglo, Australian, British, Christian, Danish, English, French, Hawaiian, Hebrew
Valley; Dean; Vindicated; Supervisor; Avenged; Judgement
Girl/Female
Muslim
Like, Equal, Matching, Observer, Supervisor
Girl/Female
Muslim/Islamic
Observer supervisor
Girl/Female
American, Anglo, Australian, British, Christian, English, Latin
Hollow; Valley; Variant of Diana; Divine; Supervisor
Boy/Male
Muslim
One who warns, Bright, Radiant, Blooming, Observer, Supervisor
Girl/Female
Indian
Warner, Observer, Supervisor
Girl/Female
Indian
Like, Equal, Matching, Observer, Supervisor
Girl/Female
Indian
Like, Equal, Matching, Observer, Supervisor
Girl/Female
Indian
Warner, Observer, Supervisor
Girl/Female
Arabic, Muslim
Guardian; Supervisor
Girl/Female
Muslim
Guardian, Supervisor
Boy/Male
Muslim
One who warns, Bright, Radiant, Blooming, Observer, Supervisor
SUPERVISED LEARNING
SUPERVISED LEARNING
Girl/Female
Hindu, Indian, Kannada, Malayalam, Marathi, Tamil, Telugu
Beautiful
Girl/Female
Bengali, Hindu, Indian, Sindhi, Tamil, Traditional
Creeper of Hope
Boy/Male
Hindu, Indian, Tamil
Who Won Wealth
Boy/Male
Hindu
Crop
Male
Chinese
genial and accomplished.
Boy/Male
Assamese, Bengali, Hindu, Indian, Kannada, Marathi, Telugu
Straightforward Person by Heart; Speech and Act
Girl/Female
Hebrew
Honey.
Girl/Female
Gujarati, Hindu, Indian, Kannada, Malayalam, Marathi, Sanskrit, Sindhi, Tamil, Telugu
Moon Like Face
Boy/Male
Indian, Punjabi, Sikh
Graceful Victory
Boy/Male
Tamil
Tarkeshwar | தாரகேஷà¯à®µà®°Â
Lord Shiva
SUPERVISED LEARNING
SUPERVISED LEARNING
SUPERVISED LEARNING
SUPERVISED LEARNING
SUPERVISED LEARNING
a.
Capable of being superposed, as one figure upon another.
imp. & p. p.
of Superpose
imp. & p. p.
of Supervene
p. pr. & vb. n.
of Supervise
n.
A man employed in a large family, or on a large estate, to manage the domestic concerns, supervise other servants, collect the rents or income, keep accounts, and the like.
n.
Supervision.
n.
The act of superposing, or the state of being superposed; as, the superposition of rocks; the superposition of one plane figure on another, in geometry.
n.
One who supervises; an overseer; an inspector; a superintendent; as, a supervisor of schools.
n.
An officer appointed to supervise the forest.
n.
One who watches over another; an overseer; a spy; a supervisor.
v. t.
To look over; to supervise.
n.
A supervisor.
imp. & p. p.
of Supervise
v. t.
Hence: To supervise; to watch over; sometimes, to observe secretly; as, to overlook a gang of laborers; to overlook one who is writing a letter.
v. t.
To look over so as to read; to peruse.
a.
Composed of superposed branches in such a way as to imitate a simple axis; as, a sympodial stem.
n.
A spectator; a looker-on.
v. t.
To survive; to outlive.
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.
Supervision; inspection.