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Solving multiple machine learning tasks at the same time
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities
Multi-task_learning
Machine learning technique
include multi-task learning, along with more formal theoretical foundations. Influential publications on transfer learning include the book Learning to Learn
Transfer_learning
applications in matrix completion, multivariate regression, and multi-task learning. Ideas of feature and group selection can also be extended to matrices
Matrix_regularization
learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of images or videos for tasks such
List of datasets in computer vision and image processing
List_of_datasets_in_computer_vision_and_image_processing
Machine learning model for speech
data. The authors found that multi-task learning improved overall performance compared to models specialized to one task. They conjectured that the best
Whisper (speech recognition system)
Whisper_(speech_recognition_system)
In functional analysis, a Hilbert space
vector-valued functions as this extension is particularly important in multi-task learning and manifold regularization. The main difference is that the reproducing
Reproducing kernel Hilbert space
Reproducing_kernel_Hilbert_space
Type of AI with wide-ranging abilities
reasoning Multi-task learning – Solving multiple machine learning tasks at the same time Neural scaling law – Statistical law in machine learning Outline
Artificial general intelligence
Artificial_general_intelligence
Overview of and topical guide to machine learning
model Multi-armed bandit Multi-task learning Multilinear subspace learning Multimodal learning Multiple instance learning Multiple-instance learning Never-Ending
Outline_of_machine_learning
Canadian computer scientist
machine learning researcher at Google Brain. At that time, he co-authored the paper "One Model to Learn Them All" about multi-task learning by a single
Aidan_Gomez
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
Problem in machine learning and statistical classification
classification One-class classification Multi-label classification Multiclass perceptron Multi-task learning In multi-label classification, OvR is known as
Multiclass_classification
Contextual queries
Kim, Bosung; Hong, Taesuk; Ko, Youngjoong; Seo, Jungyun (2020). "Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models"
Semantic_search
Representation in natural language processing
Universal Sentence Encoder Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning Barkan, Oren; Razin, Noam;
Sentence_embedding
Software library for natural language processing
classification, Entity Linking and more Statistical models for 19 languages Multi-task learning with pretrained transformers like BERT Support for custom models
SpaCy
Romanian cybersecurity technology company
trained on 60 Atari games to help other researchers with imitation and multi-task learning. In December 2023, Bitdefender launched Scamio, a free AI-powered
Bitdefender
Topics referred to by the same term
involved in learning and memory Merged transistor logic, a class of digital circuits Metric temporal logic, in computer science Multi-task learning MTL Harbor
MTL
Subset of artificial intelligence
to unseen data, and thus perform tasks without being explicitly programmed. Advances in the field of deep learning have allowed neural networks, a class
Machine_learning
Computer scientist at Emory University
models for learning and predicting across both known and unknown tasks. His research introduced directions in spatial multi-task learning, balancing the
Liang_Zhao
PMID 31390003. Zhou D, Miao L, He Y (May 2018). "Position-aware deep multi-task learning for drug-drug interaction extraction" (PDF). Artificial Intelligence
Artificial intelligence in healthcare
Artificial_intelligence_in_healthcare
type include multi-task learning (also called multi-output learning or vector-valued learning), transfer learning, and co-kriging. Multi-label classification
Kernel methods for vector output
Kernel_methods_for_vector_output
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)
Sub-field of reinforcement learning
Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that
Multi-agent reinforcement learning
Multi-agent_reinforcement_learning
Set of learning techniques in machine learning
features and use them to perform a specific task. Feature learning is motivated by the fact that ML tasks such as classification often require input that
Feature_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
Branch of machine learning
In machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation
Deep_learning
(2019). "Expression, affect, action unit recognition: Aff-wild2, multi-task learning and arcface" (PDF). British Machine Vision Conference (BMVC), 2019
List of facial expression databases
List_of_facial_expression_databases
Resource problem in machine learning
In probability theory and machine learning, the multi-armed bandit problem (sometimes called the K- or N-armed bandit problem) is named from imagining
Multi-armed_bandit
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
have extended kernel methods to handle multiple outputs, as seen in multi-task learning. The mathematical framework for kernel methods typically involves
Bayesian interpretation of kernel regularization
Bayesian_interpretation_of_kernel_regularization
2018 text-generating language model
overall score of 72.8 (compared to a previous record of 68.9) on GLUE, a multi-task test. List of large language models Open-source artificial intelligence
GPT-1
U.S. healthcare company
ensemble deep learning methods.,” J Am Med Inform Assoc, Aug. 2019. D. Zhou, L. Miao, and Y. He, “Position-aware deep multi-task learning for drug–drug
Merative
Reinforcement learning method
Shanshan (2022-08-25). "Research on Named Entity Recognition Based on Multi-Task Learning and Biaffine Mechanism". Computational Intelligence and Neuroscience
Error-driven_learning
Ability of artificial intelligence to play different games
video games Game Description Language Multi-task learning Outline of artificial intelligence Transfer learning Pell, Barney (1992). H. van den Herik;
General_game_playing
Multisite study
Suk, Heung-Ii; Shen, Dinggang (2014-01-01). "Clustering-induced multi-task learning for AD/MCI classification". Medical Image Computing and Computer-Assisted
Alzheimer's Disease Neuroimaging Initiative
Alzheimer's_Disease_Neuroimaging_Initiative
Study of viral material
(2018), Deep Learning for Genomics: A Concise Overview, arXiv:1802.00810 Seltzer, Michael L.; Droppo, Jasha (May 2013). "Multi-task learning in deep neural
Virome_analysis
Paradigm in machine learning that uses no classification labels
again with the advent of dropout, ReLU, and adaptive learning rates. A typical generative task is as follows. At each step, a datapoint is sampled from
Unsupervised_learning
Machine learning technique
in machine learning, including natural language processing tasks such as text summarization and conversational agents, computer vision tasks like text-to-image
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Statistics and machine learning technique
theoretically better. Ensemble learning trains two or more machine learning algorithms on a specific classification or regression task. The algorithms within
Ensemble_learning
Software company
spatiotemporal correlations from data for short-term traffic prediction using multi-task learning, International Symposium of Transport Simulation (ISTS’18) and the
Aimsun
American computer scientist
and multi-task learning for natural language, psychology, and medical research. Example projects include spectral learning of language models, multi-view
Lyle_Ungar
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
Type of large language model
Examples of emergent abilities include multi-step reasoning, in-context learning (the ability to perform tasks based on examples provided in prompts without
Generative pre-trained transformer
Generative_pre-trained_transformer
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
Machine learning that combines deep learning and reinforcement learning
Deep learning methods, often using supervised learning with labeled datasets, have been shown to solve tasks that involve handling complex, high-dimensional
Deep_reinforcement_learning
Structuring text as input to generative artificial intelligence
context. In addition, they trained a first single, joint, multi-task model that would answer any task-related question like "What is the sentiment" or "Translate
Prompt_engineering
Educational software application
programs, materials, or learning and development programs. The learning management system concept emerged directly from e-Learning. Learning management systems
Learning_management_system
System of multiple interacting agents
algorithmic search or reinforcement learning. With advancements in large language models (LLMs), LLM-based multi-agent systems have emerged as a new area
Multi-agent_system
Classification problem where multiple labels may be assigned to each instance
In machine learning, multi-label classification or multi-output classification is a variant of the classification problem where multiple nonexclusive
Multi-label_classification
Model of levels of increasing complexity in understanding
The structure of observed learning outcomes (SOLO) taxonomy is a model that describes levels of increasing complexity in students' understanding of subjects
Structure of observed learning outcome
Structure_of_observed_learning_outcome
Field associated with machine learning and transfer learning
it from inductive transfer learning (where labeled data is available for the target task) and unsupervised transfer learning (where labels are unavailable
Domain_adaptation
Computational model used in machine learning
Self-Supervised Learning? | Will machines ever be able to learn like humans?". Medium. Retrieved 9 June 2021. Doersch C, Zisserman A (October 2017). "Multi-task Self-Supervised
Neural network (machine learning)
Neural_network_(machine_learning)
Vectorizing features using a hash function
In a typical document classification task, the input to the machine learning algorithm (both during learning and classification) is free text. From
Feature_hashing
2020 text-generating language model
tokens, and has demonstrated strong "zero-shot" and "few-shot" learning abilities on many tasks. On September 22, 2020, Microsoft announced that it had licensed
GPT-3
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
Weak_supervision
American-German economist (born 1950)
1016/S0014-2921(01)00086-1. Snower, Dennis; Lindbeck, Assar (2000). "Multi-task Learning and the Reorganization of Work". Journal of Labor Economics. 18 (3):
Dennis_Snower
Machine learning methods using multiple input modalities
tasks like visual question answering, cross-modal retrieval, text-to-image generation, aesthetic ranking, and image captioning. Multimodal learning was
Multimodal_learning
Ensemble learning method
popular and effective technique used in supervised learning for both classification and regression tasks. The theoretical foundation for boosting came from
Boosting_(machine_learning)
Dimensionality reduction of graph-based semantic data objects [machine learning task]
representation learning, knowledge graph embedding (KGE), also called knowledge representation learning (KRL), or multi-relation learning, is a machine learning task
Knowledge_graph_embedding
Range of neurodevelopmental conditions
affected by a learning disability. People with a learning disability have trouble performing specific types of skills or completing tasks if left to figure
Learning_disability
Decentralized machine learning
Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients)
Federated_learning
Professor of biostatistics
and graphical models. Machine learning topics including transfer learning, multi-task learning, and federated learning. Network science including community
Yang_Feng_(statistician)
Computerized information extraction from images
computer vision. The accuracy of deep learning algorithms on several benchmark computer vision data sets for tasks ranging from classification, segmentation
Computer_vision
Subfield of artificial intelligence
data sets and online machine learning There are many reasons for wanting to distribute intelligence or cope with multi-agent systems. Mainstream problems
Distributed artificial intelligence
Distributed_artificial_intelligence
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
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
Learning that occurs through observing the behaviour of others
Observational learning is learning that occurs through observing the behavior of others. It is a form of social learning which takes various forms, based
Observational_learning
Faculty of mind to store and retrieve data
congruent with the original learning task (i.e., in the same room), memory impairment and the detrimental effects of stress on learning can be attenuated. Seventy-two
Memory
Tasks in machine learning
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function
Training, validation, and test data sets
Training,_validation,_and_test_data_sets
Processing of natural language by a computer
annotated and non-annotated data. Generally, this task is much more difficult than supervised learning, and typically produces less accurate results for
Natural_language_processing
Ability to perform activities simultaneously
in a learning environment are worse at learning new information compared to those who do not have their attention divided among different tasks. The first
Human_multitasking
American software company
According to the company, Synopsys engineers began developing reinforcement learning applications for electronic design automation in 2017, which led to the
Synopsys
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)
Learning theory involving the construction of mental models
strategies make problem-based learning more effective: The learning activities should be related to a larger task. The larger task is important because it allows
Constructionism (learning theory)
Constructionism_(learning_theory)
Interdisciplinary research area
machine learning (QML) is the study of quantum algorithms for machine learning. It often refers to quantum algorithms for machine learning tasks which analyze
Quantum_machine_learning
Set of methods for supervised statistical learning
regression and linear regression. Classifying data is a common task in machine learning. Suppose some given data points each belong to one of two classes
Support_vector_machine
"GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding". arXiv:1804.07461 [cs.CL]. "Computers Are Learning to Read—But
List of datasets for machine-learning research
List_of_datasets_for_machine-learning_research
Computer system simulating intelligence
and, in particular, multi-objective evolutionary optimization Swarm intelligence Bayesian networks Artificial immune systems Learning theory Probabilistic
Computational_intelligence
2017 research paper by Google
research paper in machine learning authored by eight scientists and engineers working at Google. The paper introduced a new deep learning architecture known
Attention_Is_All_You_Need
Mathematical concept
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute
Multi-objective_optimization
Type of database that uses vectors to represent other data
databases are used in a wide range of machine learning applications including similarity search, semantic search, multi-modal search, recommendations engines
Vector_database
Largely debunked theories that aim to account for differences in individuals' learning
Learning, and Cognitive Styles. In the 1980s, the National Association of Secondary School Principals (NASSP) formed a task force to study learning styles
Learning_styles
Method in natural language processing
of kernel CCA to bilingual (and multi-lingual) corpora, also providing an early example of self-supervised learning of word embeddings. Word embeddings
Word_embedding
Continuous performance task
The n-back task is a continuous performance task that is commonly used as an assessment in psychology and cognitive neuroscience to measure a part of
N-back
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
Automated recognition of patterns and regularities in data
Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is
Pattern_recognition
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)
Artificial intelligence model paradigm
other through standardized task benchmarks like MMLU, MMMU, HumanEval, and GSM8K. Given that foundation models are multi-purpose, increasingly meta-benchmarks
Foundation_model
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
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
Type of feedforward neural network
wrappers. Deeplearning4j: Deep learning in Java and Scala on multi-GPU-enabled Spark. A general-purpose deep learning library for the JVM production stack
Convolutional_neural_network
Use of technology in education to enhance learning and teaching
communication, cyber-learning, and multi-modal instruction, virtual education, personal learning environments, networked learning, virtual learning environments
Educational_technology
Intelligence of machines
capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception,
Artificial_intelligence
Language models designed for reasoning tasks
introduced Deep Research in Gemini, a feature designed to conduct multi-step research tasks. On December 16, 2024, researchers demonstrated that by scaling
Reasoning_model
Software for understanding biological data
Machine learning algorithms in bioinformatics can be used for prediction, classification, and feature selection. Methods to achieve this task are varied
Machine learning in bioinformatics
Machine_learning_in_bioinformatics
Type of machine learning model
the emergent abilities is in-context learning from example demonstrations. In-context learning is involved in tasks, such as: reported arithmetics decoding
Large_language_model
Swiss physicist
dynamic environmental information and apply it to communication and task learning among other robots. In 1999, Billard was appointed research associate
Aude_Billard
Difficulty keeping organised to complete tasks
difficulty with initiating new non-routine tasks. Although an estimated 25–40% of people with autism also have a learning disability, many will demonstrate an
Executive_dysfunction
Self-awareness about thinking, higher-order thinking skills
over the process in learning situations. The skills that aid in regulation involve planning the way to approach a learning task, monitoring comprehension
Metacognition
Concept in software engineering and computer science
interaction Smart city (ubiquitous city) Ubiquitous commerce Ubiquitous learning Ubiquitous robot Wearable computer Nieuwdorp, E. (2007). "The pervasive
Ubiquitous_computing
Reinforcement learning technique
reinforcement learning agents. Intuitively, agents learn to improve their performance by playing "against themselves". In multi-agent reinforcement learning experiments
Self-play
MULTI TASK-LEARNING
MULTI TASK-LEARNING
Boy/Male
Muslim
Acme of mountain
Boy/Male
Hindu
Mukti, Emancipation, Liberation
Boy/Male
Hindu, Indian, Jain, Marathi
With Multi-coloured Body
Surname or Lastname
German and Dutch
German and Dutch : from a pet form of the personal name Thomas.English : unexplained.
Surname or Lastname
English
English : habitational name for someone from Thirsk in North Yorkshire, named from an unattested Old Scandinavian word, thresk ‘marsh’.
Boy/Male
Arabic, Muslim, Pashtun
Acme of Mountain
Female
Chamoru
, bay, ocean, sea.
Surname or Lastname
English
English : topographic name for someone who lived by an ash tree, from the Middle English phrase at(te) asche ‘at (the) ash’.Jewish (Ashkenazic) : metonymic occupational name for a maker or seller of bags and purses, from German Tasche ‘bag’, ‘purse’. Compare Taschner.
Boy/Male
Gujarati, Hindu, Indian
Son of Bharat (Brother of Lord Rama)
Boy/Male
Muslim
Jurist
Boy/Male
Hindu, Indian, Marathi
Multi Talented Person; With Good Taste
Boy/Male
Tamil
Mukti, Emancipation, Liberation
Boy/Male
Tamil
Chirtrang | சிரà¯à®¤à¯à®°à®‚க
With multi-colored body
Chirtrang | சிரà¯à®¤à¯à®°à®‚க
Boy/Male
Hindu
With multi-colored body
Surname or Lastname
Swedish and Norwegian
Swedish and Norwegian : from ask ‘ash tree’, applied either as a habitational name from a place named with this word or as an ornamental name.English : habitational name from a place in North Yorkshire named Aske, from Old English as æsc ‘ash tree’, later replaced by the Old Norse cognate askr.
Girl/Female
Indian, Punjabi, Sikh
Multi Talented
Surname or Lastname
English
English : from a medieval vernacular short form of the personal name Pascal, Latin Paschalis (see Pascal).
Girl/Female
Hindu
Salvation, Freedom from life and death
Girl/Female
Spanish
Task.
Girl/Female
Hindu
A creeper with fragrant flowers
MULTI TASK-LEARNING
MULTI TASK-LEARNING
Girl/Female
Biblical
Hairy, goat, demon, tempest.
Girl/Female
Indian
Bright, Masculine zealand
Boy/Male
American, British, English
From the Mill Stream
Boy/Male
Tamil
Religious
Surname or Lastname
English
English : patronymic meaning ‘son of the beadle’ (see Beadle).
Girl/Female
Tamil
Purnima | பூரà¯à®£à®¿à®®à®¾
Full Moon
Girl/Female
Indian
Jasmine or flower
Boy/Male
British, English, German
From the Wooden Valley
Boy/Male
Hindu, Indian, Malayalam
One who Wears Snake; Lord Shiva
Boy/Male
Indian
Falcon, Hawk (Garuda)
MULTI TASK-LEARNING
MULTI TASK-LEARNING
MULTI TASK-LEARNING
MULTI TASK-LEARNING
MULTI TASK-LEARNING
v. t.
To consume or spend in talking; -- often followed by away; as, to talk away an evening.
v. i.
To wear a mask; to be disguised in any way.
n.
A cover, or partial cover, for the face, used for disguise or protection; as, a dancer's mask; a fencer's mask; a ball player's mask.
v. t.
To deliver in talking; to speak; to utter; to make a subject of conversation; as, to talk nonsense; to talk politics.
n.
The quantity contained in a cask.
v. t.
To put into a cask.
n.
See 2d Tusk, n., 2.
v. t.
To speak freely; to use for conversing or communicating; as, to talk French.
n.
A peculiar flavor or taint; as, a musty tack.
v. t.
To impose a task upon; to assign a definite amount of business, labor, or duty to.
pl.
of Mufti
n.
A toothshell, or Dentalium; -- called also tusk-shell.
n.
Report; rumor; as, to hear talk of war.
n.
That which is attached; a supplement; an appendix. See Tack, v. t., 3.
n.
Subject of discourse; as, his achievment is the talk of the town.
v. t.
To invite; as, to ask one to an entertainment.
v. t.
The direction of a vessel in regard to the trim of her sails; as, the starboard tack, or port tack; -- the former when she is closehauled with the wind on her starboard side; hence, the run of a vessel on one tack; also, a change of direction.
v. t.
Especially, to attach or secure in a slight or hasty manner, as by stitching or nailing; as, to tack together the sheets of a book; to tack one piece of cloth to another; to tack on a board or shingle; to tack one piece of metal to another by drops of solder.