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Set of learning techniques in machine learning
In machine learning (ML), representation learning or feature learning is a set of techniques that allow a system to automatically discover the representations
Representation_learning
Area of discrete mathematics
cannot be coupled to a certain representation. The way it is represented depends on the degree of convenience such representation provides for a certain application
Graph_theory
Multimodal representation learning is a subfield of representation learning focused on integrating and interpreting information from different modalities
Multimodal representation learning
Multimodal_representation_learning
Algorithm for modelling sequential data
encoder-decoder variants, depending on whether they are optimized for representation learning, autoregressive generation, or conditional sequence-to-sequence
Transformer_(deep_learning)
Representation learning technique
In machine learning, embedding is a representation learning technique that maps complex, high-dimensional data into a lower-dimensional vector space of
Embedding_(machine_learning)
Subset of artificial intelligence
AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. By 1980, expert
Machine_learning
Branch of machine learning
networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and revolves
Deep_learning
Machine learning paradigm
used for representation learning. Autoencoders consist of an encoder network that maps the input data to a lower-dimensional representation (latent space)
Self-supervised_learning
Representation learning method
Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims to find a sparse representation of the input
Sparse_dictionary_learning
Type of knowledge base
in data science and machine learning, particularly in graph neural networks, representation learning, and machine learning, have broadened the scope of
Knowledge_graph
Phase transition in machine learning
(2022). "Towards Understanding Grokking: An Effective Theory of Representation Learning". In Koyejo, Sanmi; Mohamed, S.; Agarwal, A.; Belgrave, Danielle;
Grokking_(machine_learning)
Overview of and topical guide to deep learning
neural networks with multiple processing layers. It emphasizes representation learning and is widely used in areas such as computer vision, natural language
Outline_of_deep_learning
2016 browser game by Google LLC
Han (November 3, 2023). "A Generic Self-Supervised Learning (SSL) Framework for Representation Learning from Spectral Spatial Features of Unlabeled Remote
Quick,_Draw!
Phenomenon in machine learning
Representation collapse is a phenomenon in machine learning and representation learning where a model maps different inputs to the same or very similar
Representation_collapse
Deep learning generative model to encode data representation
Artificial neural network Deep learning Generative adversarial network Representation learning Sparse dictionary learning Data augmentation Backpropagation
Variational_autoencoder
Slovak computer scientist and AI researcher
self-supervised representation-learning method. In 2023, he and his co-authors received an ICML Outstanding Paper Award for work on learning in zero-sum
Michal_Valko
Concept in education
learning models and representations relating different degrees of retention induced from various types of learning. The earliest such representation is
Learning_pyramid
Class of artificial neural networks
spatial or feature similarity. This graph-based representation enables the application of graph learning models to visual tasks. The relational structure
Graph_neural_network
Internal representation of world by AI
world model in artificial intelligence is a machine learning system that builds an internal representation of an environment. Often this is via understanding
World model (artificial intelligence)
World_model_(artificial_intelligence)
Processing of natural language by a computer
Word2vec. In the 2010s, representation learning and deep neural network-style (featuring many hidden layers) machine learning methods became widespread
Natural_language_processing
Dimensionality reduction of graph-based semantic data objects [machine learning task]
In representation learning, knowledge graph embedding (KGE), also called knowledge representation learning (KRL), or multi-relation learning, is a machine
Knowledge_graph_embedding
Theories that aim to account for differences in individuals' learning
Learning styles refer to a range of theories that aim to account for differences in individuals' learning. Although there is ample evidence that individuals
Learning_styles
Machine learning technique
In deep learning, fine-tuning is the process of adapting a computational model trained for one task (the upstream task) to perform a different, usually
Fine-tuning_(deep_learning)
Embedding of data within a manifold based on a similarity function
trends in academic research from a citation network using network representation learning". PLOS ONE. 13 (5) e0197260. Bibcode:2018PLoSO..1397260A. doi:10
Latent_space
Research field in deep learning
deep learning (TDL) is a research field that extends deep learning to handle complex, non-Euclidean data structures. Traditional deep learning models
Topological_deep_learning
Computer scientist and professor
research is in computer vision and machine learning, including optical flow, visual representation learning and deep neural networks. He co-authored the
Thomas_Brox
improve relationships between individuals in the workplace. In graph representation learning, heterophily means that nodes from different classes are more likely
Heterophily
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
Canadian computer scientist
concerns reinforcement learning and representation learning for adaptive autonomous agents, including Temporal difference learning and optimization in semisupervised
Martha White (computer scientist)
Martha_White_(computer_scientist)
Machine learning model for vision processing
Chelsea; Sadigh, Dorsa; Liang, Percy (2023-02-24), Language-Driven Representation Learning for Robotics, arXiv:2302.12766 Touvron, Hugo; Cord, Matthieu; Sablayrolles
Vision_transformer
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
Machine learning technique
Predictive learning is a machine learning (ML) technique where an artificial intelligence model is fed new data to develop an understanding of its environment
Predictive_learning
Method in natural language processing
dimensionality of word representations in contexts by "learning a distributed representation for words". A study published in NeurIPS (NIPS) 2002 introduced
Word_embedding
List of concepts in artificial intelligence
reinforcement learning. It can be used for example to make the generative AI model more truthful or less harmful. representation learning See feature learning. reservoir
Glossary of artificial intelligence
Glossary_of_artificial_intelligence
Machine learning technique
Simeng; Ginsburg, Boris (2024). "NGPT: Normalized Transformer with Representation Learning on the Hypersphere". arXiv:2410.01131 [cs.LG]. Chen, Zhao; Badrinarayanan
Normalization (machine learning)
Normalization_(machine_learning)
Representation of a computer program
computer science, a code property graph (CPG) is a computer program representation that captures syntactic structure, control flow, and data dependencies
Code_property_graph
Family of machine learning techniques
example of in-batch top-K selection appears in unsupervised video representation learning, where the negatives incurring the highest triplet loss within
Hard_negative_mining
Intelligence in machines
videos). The traditional goals of AI research include learning, reasoning, knowledge representation, planning, natural language processing, and perception
Artificial_intelligence
Use of artificial intelligence in the automation of electronic design
the design process, as seen in the FIST tool. A major use is in representation learning, where the aim is to automatically learn useful and often simpler
AI-driven_design_automation
Theory of brain function
the rising popularity of representation learning, the theory has also been actively pursued and applied in machine learning and related fields. Recent
Predictive_coding
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
Vietnamese-American computer scientist (born 1982)
collaboration with Tomáš Mikolov, Le developed the doc2vec model for representation learning of documents. Le was also a key contributor of Google Neural Machine
Quoc_V._Le
Type of artificial neural network
learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with
Extreme_learning_machine
Machine learning framework
Machine Learning. PMLR: 2642–2651. arXiv:1610.09585. Radford, Alec; Metz, Luke; Chintala, Soumith (2016). "Unsupervised Representation Learning with Deep
Generative adversarial network
Generative_adversarial_network
Field of artificial intelligence
Knowledge representation (KR) aims to model information in a structured manner to formally represent it as knowledge in knowledge-based systems whereas
Knowledge representation and reasoning
Knowledge_representation_and_reasoning
Deep neural network for generating raw audio
the other. The January 2019 follow-up paper Unsupervised speech representation learning using WaveNet autoencoders details a method to successfully enhance
WaveNet
Russian computer scientist
Algorithms and Machine Learning (CAML) at Indiana University. Yaroslavtsev is best known for his work on representation learning and optimization in AI
Grigory_Yaroslavtsev
Voice conversion software
Quantization and Mutual Information-Based Unsupervised Disentangled Representation Learning for One-Shot Voice Conversion (PDF). Proc. Interspeech. pp. 566–570
Retrieval-based Voice Conversion
Retrieval-based_Voice_Conversion
Series of language models developed by Google AI
The latent vector representation of the model is directly fed into this new module, allowing for sample-efficient transfer learning. This section describes
BERT_(language_model)
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
Neural network that learns efficient data encoding in an unsupervised manner
behavior of real-world channels. Representation learning Singular value decomposition Sparse dictionary learning Deep learning Bank, Dor; Koenigstein, Noam;
Autoencoder
AI Foundation model for tabular data
2024). Adapting TabPFN for Zero-Inflated Metagenomic Data. Table Representation Learning Workshop at NeurIPS 2024. Wu, Dan-Ni (2026). "PanMETAI: a high
TabPFN
Relationship between proficiency and experience
a learning curve Proficiency (test score)Experience (hours spent)01234503691215Proficiency (test score)Example of a steep learning curve A learning curve
Learning_curve
Neural network working on two input vectors
Siamese Networks for Object Tracking arXiv:1606.09549 "End-to-end representation learning for Correlation Filter based tracking". "Structured Siamese Network
Siamese_neural_network
Type of artificial intelligence system
Li, Zhen (2021-06-11). "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision". arXiv:2102.05918 [cs.CV]. Jaegle
Vision-language_model
Reverse-engineering neural networks
linear representation hypothesis and the geometry of large language models". Proceedings of the 41st International Conference on Machine Learning. Proceedings
Mechanistic_interpretability
Problem setup in machine learning
Zero-shot learning (ZSL) is a problem setup in machine learning where, at test time, a learner observes samples from classes which were not observed during
Zero-shot_learning
Deep learning artificial intelligence research team
Phielipp M, Goldberg K (May 2020). "Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos". 2020 IEEE International Conference on Robotics
Google_Brain
Technique in neural networks for learning joint representations of text and images
Vision-Language Representation Learning With Noisy Text Supervision". Proceedings of the 38th International Conference on Machine Learning. PMLR: 4904–4916
Contrastive Language–Image Pre-training
Contrastive_Language–Image_Pre-training
Artificial intelligence model developed by TypeSafe AI
executive, spent approximately four years at OpenAI working on reinforcement learning from human feedback (RLHF), InstructGPT, ChatGPT and GPT-4 before leaving
Jev_(AI_model)
American computer scientist
DeepWalk, a method that incorporates text features into network representation learning and outperforms other baselines on multi-class classification tasks
Edward_Y._Chang
common model (representation). Compared to information integration, which involves merging information having different schemas and representation models, knowledge
Knowledge_integration
Methods in artificial intelligence research
to solve a wide variety of problems, including knowledge representation, planning and learning. Logic was also the focus of the work at the University
Symbolic artificial intelligence
Symbolic_artificial_intelligence
Characterizes a recent neural network architecture providing advances in time series deep learning
Zhu, L., et al. (2024). "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model". arXiv:2401.09417 Goel, K
State space model (deep learning)
State_space_model_(deep_learning)
Subfield of artificial intelligence
requirements and knowledge reuse, the representation capacity of neural models of computation, the principled combination of learning and reasoning using the interplay
Neuro-symbolic_AI
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
Disorder affecting learning arithmetic
a learning disorder, resulting in difficulty learning or comprehending arithmetic, such as difficulty in understanding numbers, numeracy, learning how
Dyscalculia
Mental representation of the external world
A mental model is an internal representation of external reality: that is, a way of representing reality within the mind. Such models are hypothesized
Mental_model
Range of neurodevelopmental conditions
Learning disability, or learning disorder, is a condition in the brain that causes difficulties comprehending or processing information and can be caused
Learning_disability
search, automated reasoning, knowledge representation and reasoning, planning, machine learning, deep learning, natural language processing, computer
List of artificial intelligence algorithms
List_of_artificial_intelligence_algorithms
Node graph framework
Avishek (2020). "A Comparative Study for Unsupervised Network Representation Learning". IEEE Transactions on Knowledge and Data Engineering: 1. arXiv:1903
Node2vec
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
Machine learning technique
paper on transfer learning in neural networks, 1976". Informatica 44: 291–302. S. Bozinovski (1981). "Teaching space: A representation concept for adaptive
Transfer_learning
Slovene computer scientist
William L. Hamilton; Rex Ying; Jure Leskovec (2017). "Inductive Representation Learning on Large Graphs" (PDF). Advances in Neural Information Processing
Jure_Leskovec
Architectural motif in neural networks for aggregating information
Chelsea; Sadigh, Dorsa; Liang, Percy (2023-02-24). "Language-Driven Representation Learning for Robotics". arXiv:2302.12766 [cs.RO]. Gao, Hongyang; Ji, Shuiwang
Pooling_layer
Biological theory of intelligence
distributive representation" where only about 2% of the columns are active at any given time. An HTM attempts to model a portion of the cortex's learning and plasticity
Hierarchical_temporal_memory
Model of algorithmic learning
computational learning theory, Occam learning is a model of algorithmic learning where the objective of the learner is to output a succinct representation of received
Occam_learning
Branch of statistics
estimated by bootstrap re-sampling. Recent advancements in deep representation learning have been extended to survival estimation. The DeepSurv model proposes
Survival_analysis
Obtaining information resources relevant to an information need across modalities
systems employ various techniques: Common representation learning: The most prevalent approach involves learning a shared embedding space where items from
Cross-modal_retrieval
American computer scientist
articulatory modeling, to speech representation learning, and to bridging the gaps between speech research, machine learning and natural language processing"
Karen_Livescu
Method of machine learning
In computer science, incremental learning is a method of machine learning in which input data is continuously used to extend the existing model's knowledge
Incremental_learning
Concept that UK parliamentarians spoke on behalf of all imperial subjects
The concept of virtual representation proposed that the members of the UK Parliament, including the Lords and the Crown-in-Parliament, reserved the right
Virtual_representation
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
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
Automatic repair of software bugs
Jacques; Bissyandé, Tegawendé F. (27 January 2021). "Evaluating representation learning of code changes for predicting patch correctness in program repair"
Automatic_bug_fixing
Paradigm in machine learning
representation. Iteratively refining the representation and then performing semi-supervised learning on said representation may further improve performance. Self-training
Weak_supervision
Machine learning strategy
exploitation over the data space representation. This strategy manages this compromise by modelling the active learning problem as a contextual bandit problem
Active learning (machine learning)
Active_learning_(machine_learning)
Subdiscipline of artificial intelligence
strictly limited to learning aspects; it is equally concerned with reasoning (specifically probabilistic inference) and knowledge representation. Therefore, alternative
Statistical relational learning
Statistical_relational_learning
Concept in natural language processing
(2022). An eye on representation learning in ontology matching. OM@ISWC. Pekar, Viktor; Staab, Steffen (2002). Taxonomy learning. Proceedings of the
Semantic_similarity
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)
Slovenian computer scientist
Horwood, 1994 Foundations of Rule Learning, with Johannes Fürnkranz and Dragan Gamberger, Springer, 2012 Representation Learning: Propositionalization and Embeddings
Nada_Lavrač
Computational model used in machine learning
Doersch C, Gupta A, Efros AA (December 2015). "Unsupervised Visual Representation Learning by Context Prediction". 2015 IEEE International Conference on Computer
Neural network (machine learning)
Neural_network_(machine_learning)
Decline of memory retention in time
of learning. The constants c and k are 1.25 and 1.84 respectively. Savings is defined as the relative amount of time saved on the second learning trial
Forgetting_curve
Text-structure representation using graph models
Surveillance systems, etc. Graph-based methods for NLP and Semantic Web Representation learning methods for knowledge graphs (i.e., knowledge graph embedding)
Text_graph
Measure used to compare treatments in randomised trials
arbitrary regression frameworks as base learners to infer the CATE. Representation learning can be used to further improve the performance of these methods
Average_treatment_effect
Type of artificial intelligence model trained on Earth observation and geoscientific data
Cheng, Yu; Liu, Jingjing (2019). "UNITER: UNiversal Image-TExt Representation Learning". arXiv:1909.11740 [cs.CV]. Li, Xiujun; Yin, Xi; Li, Chunyuan;
Geospatial_foundation_model
Overview of and topical guide to machine learning
is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence within computer
Outline_of_machine_learning
Process by which people befriend similar people
decreased influence on fertility rates in such populations. In graph representation learning, homophily means that nodes with the same label or attributes are
Homophily
foundational contributions uniting deep generative models, representation learning, and reinforcement learning, and for their applications in advancing scientific
IJCAI Computers and Thought Award
IJCAI_Computers_and_Thought_Award
Syllabic script used for writing Mycenaean Greek
Ariadne, pp. 359–379, 2025 Srivatsan, Nikita, et al., "Neural Representation Learning for Scribal Hands of Linear B", International Conference on Document
Linear_B
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