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Parameter controlling the machine learning process
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters
Hyperparameter (machine learning)
Hyperparameter_(machine_learning)
Process of finding the optimal set of variables for a machine learning algorithm
In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter
Hyperparameter_optimization
Process of automating the application of machine learning
used in AutoML include hyperparameter optimization, meta-learning and neural architecture search. In a typical machine learning application, practitioners
Automated_machine_learning
Topics referred to by the same term
Hyperparameter may refer to: Hyperparameter (machine learning) Hyperparameter (Bayesian statistics) This disambiguation page lists articles associated
Hyperparameter
Tuning parameter (hyperparameter) in optimization
which are generally built into deep learning libraries such as Keras. Hyperparameter (machine learning) Hyperparameter optimization Stochastic gradient descent
Learning_rate
Algorithm for modelling sequential data
convention. It was difficult to train and required careful hyperparameter tuning and a "warm-up" in learning rate, where it starts small and gradually increases
Transformer_(deep_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
Overview of and topical guide to machine learning
Explanation-based learning Feature GloVe Hyperparameter Inferential theory of learning Learning automata Learning classifier system Learning rule Learning with errors
Outline_of_machine_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
Machine learning technique
It was difficult to train, and required careful hyperparameter tuning and a "warm-up" in learning rate, where it starts small and gradually increases
Normalization (machine learning)
Normalization_(machine_learning)
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
Computational model used in machine learning
2019 – via autokeras.com. Claesen M, De Moor B (2015). "Hyperparameter Search in Machine Learning". arXiv:1502.02127 [cs.LG]. Bibcode:2015arXiv150202127C
Neural network (machine learning)
Neural_network_(machine_learning)
Statistical optimization technique
optimization algorithms have found prominent use in machine learning problems for optimizing hyperparameter values. The term is generally attributed to Jonas
Bayesian_optimization
Decentralized machine learning
federated learning process (in addition to the machine learning model's own hyperparameters) to optimize learning: Number of federated learning rounds:
Federated_learning
Machine learning technique
by others. Catastrophic forgetting Continual learning Domain adaptation Foundation model Hyperparameter optimization Overfitting von Csefalvay, Chris
Fine-tuning_(deep_learning)
Machine learning technique
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Subset of artificial intelligence
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn
Machine_learning
Hyperparameter optimization framework
Optuna is an open-source Python library for automatic hyperparameter tuning of machine learning models. It was first introduced in 2018 by Preferred Networks
Optuna
Measurement of algorithmic bias
Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions
Fairness_(machine_learning)
Type of feedforward neural network
(-\infty ,\infty )} . Hyperparameters are various settings that are used to control the learning process. CNNs use more hyperparameters than a standard multilayer
Convolutional_neural_network
Algorithm for obtaining vector representations of words
}}\end{array}}\right.} and x max , α {\displaystyle x_{\max },\alpha } are hyperparameters. In the original paper, the authors found that x max = 100 , α = 3
GloVe
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
Machine learning technique
Mixture of experts (MoE) is a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous
Mixture_of_experts
Microsoft toolkit for hyperparameter tuning and neural architecture search MindsDB – AutoML platform that embeds machine learning into SQL databases and
Lists of open-source artificial intelligence software
Lists_of_open-source_artificial_intelligence_software
Tasks in machine learning
and test data sets. The standard machine learning practice is to train on the training set and tune hyperparameters using the validation set, where the
Training, validation, and test data sets
Training,_validation,_and_test_data_sets
Engineering applied to artificial intelligence
most suitable machine learning algorithm, including deep learning paradigms. Once an algorithm is chosen, optimizing it through hyperparameter tuning is essential
Artificial intelligence engineering
Artificial_intelligence_engineering
Optimization algorithm
hyperparameters, i.e. a fixed learning rate and momentum parameter. In the 2010s, adaptive approaches to applying SGD with a per-parameter learning rate
Stochastic_gradient_descent
{R} ^{D}} . The learning rate at iteration step t {\displaystyle t} is denoted by α t {\displaystyle \alpha _{t}} . The hyperparameters w {\displaystyle
Learning_vector_quantization
German computer scientist
to machine learning, particularly in the areas of automated machine learning (AutoML), hyperparameter optimization, meta-learning and tabular machine learning
Frank_Hutter
Solving multiple machine learning tasks at the same time
multi-tasking has led to advances in automatic hyperparameter optimization of machine learning models and ensemble learning. Applications have also been reported
Multi-task_learning
Suite of machine learning software written in Java
Waikato Environment for Knowledge Analysis (Weka) is a collection of machine learning and data analysis free software licensed under the GNU General Public
Weka_(software)
\mathbb {M} } with parameter vector w {\displaystyle w} and a so-called hyperparameter or regularization parameter λ {\displaystyle \lambda } , Bayesian inference
Least-squares support vector machine
Least-squares_support_vector_machine
Overview of and topical guide to deep learning
Hyperparameter Hyperparameter optimization Foundation model Large language model Supervised learning Unsupervised learning Self-supervised learning Semi-supervised
Outline_of_deep_learning
Reinforcement learning algorithms
variance, no bias) and 1-step TD learning ( λ = 0 {\displaystyle \lambda =0} , low variance, high bias). This hyperparameter can be adjusted to pick the optimal
Actor-critic_algorithm
Artificial intelligence algorithm
A Tsetlin machine is an artificial intelligence algorithm based on propositional logic. A Tsetlin machine is a form of learning automaton collective for
Tsetlin_machine
Machine learning engine service
Enterprise Agent Platform (formerly known as Vertex AI) is a managed machine learning (ML) and artificial intelligence (AI) platform developed by Google
Gemini Enterprise Agent Platform
Gemini_Enterprise_Agent_Platform
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
Object categorization problem
One-shot learning is an object categorization problem, found mostly in computer vision. Whereas most machine learning-based object categorization algorithms
One-shot learning (computer vision)
One-shot_learning_(computer_vision)
Additionally, it contains feature engineering, model chaining, and hyperparameter optimization. Jio Brain offers mobile and enterprise-ready LLM-as-a-service
Artificial intelligence in India
Artificial_intelligence_in_India
Type of activation function
e^{x}&x\leq 0\end{cases}}} In these formulas, α {\displaystyle \alpha } is a hyperparameter to be tuned with the constraint α ≥ 0 {\displaystyle \alpha \geq 0}
Rectified_linear_unit
Model-free reinforcement learning algorithm
_{0}} , initial value function parameters ϕ 0 {\textstyle \phi _{0}} Hyperparameters: KL-divergence limit δ {\textstyle \delta } , backtracking coefficient
Proximal_policy_optimization
Open-source machine learning platform
development of machine learning models, the Katib component. It is described as a Kubernetes-native project and features hyperparameter tuning, early stopping
Kubeflow
Machine learning-powered structure design
related to hyperparameter optimization and meta-learning and is a subfield of automated machine learning (AutoML). Reinforcement learning (RL) can underpin
Neural_architecture_search
Statistical law in machine learning
}=0} . Secondary effects also arise due to differences in hyperparameter tuning and learning rate schedules. Kaplan et al.: used a warmup schedule that
Neural_scaling_law
Subfield of control engineering
Enrico (December 2016). "Feature vector regression with efficient hyperparameters tuning and geometric interpretation". Neurocomputing. 218: 411–422
Fault_detection_and_isolation
Reinforcement learning method
the choice of the error function, the learning rate, the initialization of the weights, and other hyperparameters, which can affect the convergence and
Error-driven_learning
Machine learning algorithm
(SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning. It was proposed by Rummery
State–action–reward–state–action
State–action–reward–state–action
Machine learning system
passes bootstrapping User settable online learning progress report + auditing of the model Hyperparameter optimization Vowpal wabbit has been used to
Vowpal_Wabbit
Statistical model
coordinate of estimation x* and all other observed coordinates x for a given hyperparameter vector θ, K ( θ , x , x ′ ) {\displaystyle K(\theta ,x,x')} and
Gaussian_process
distribution Hypergeometric distribution Hyperparameter (Bayesian statistics) Hyperparameter (machine learning) Hyperprior Hypoexponential distribution
List_of_statistics_articles
List of concepts in artificial intelligence
hyperparameter A parameter that can be set in order to define any configurable part of a machine learning model's learning process. hyperparameter optimization
Glossary of artificial intelligence
Glossary_of_artificial_intelligence
Software tool
Studio also has a library of loss functions and optimizers for use in hyperparameter tuning, a traditionally complicated area in neural network programming
Deep_Learning_Studio
Generative adversarial network variant
stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches".
Wasserstein_GAN
minimization (SRM) is an inductive principle of use in machine learning. Commonly in machine learning, a generalized model must be selected from a finite
Structural_risk_minimization
Class of nonparametric methods
In machine learning, the kernel embedding of distributions (also called the kernel mean or mean map) comprises a class of nonparametric methods in which
Kernel embedding of distributions
Kernel_embedding_of_distributions
Techniques for lossy compression of neural networks
Model compression is a machine learning technique for reducing the size of trained models. Large models can achieve high accuracy, but often at the cost
Model_compression
Open source version system
a free and open-source, platform-agnostic version system for data, machine learning models, and experiments. It is designed to make ML models shareable
Data Version Control (software)
Data_Version_Control_(software)
Technique for shaping training datasets
In machine learning, manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that
Manifold_regularization
Neural network technology
detecting a specific feature in the input data. The size of the kernel is a hyperparameter that affects the network's behavior. For a 2D input x {\displaystyle
Convolutional_layer
Function for machine learning algorithms
Triplet loss is a machine learning loss function widely used in one-shot learning, a setting where models are trained to generalize effectively from limited
Triplet_loss
Computational geometry and optimization concept
objective value. They are also used in: Support vector machines Subspace approximation Hyperparameter optimization More recently, coresets have been explored
Coreset
Statistical model validation technique
Conference on Machine Learning. pp. 39–44. ISBN 978-94-6197-044-2. Soper, Daniel S. (16 August 2021). "Greed Is Good: Rapid Hyperparameter Optimization
Cross-validation_(statistics)
Influential 2012 deep convolutional neural network
Krizhevsky's bedroom at his parents' house. During 2012, Krizhevsky performed hyperparameter optimization on the network until it won the ImageNet competition later
AlexNet
AI Foundation model for tabular data
TabPFN is pre-trained, in contrast to other deep learning methods, it does not require costly hyperparameter optimization. TabPFN is the subject of on-going
TabPFN
2023 text-generating language model
dataset was constructed, the computing power required, or any hyperparameters such as the learning rate, epoch count, or optimizer(s) used. The report claimed
GPT-4
Property of a model
Hyperparameter optimization Law of total variance Minimum-variance unbiased estimator Model selection Regression model validation Supervised learning
Bias–variance_tradeoff
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
Projection of data onto lower-dimensional manifolds
nonzero eigen vectors provide an orthogonal set of coordinates. The only hyperparameter in the algorithm is what counts as a "neighbor" of a point. Generally
Nonlinear dimensionality reduction
Nonlinear_dimensionality_reduction
German computer scientist
umbrella of automating parts of the Machine Learning pipeline. His research touches many different aspects: Hyperparameter Optimization Multi-Fidelity Optimization
Marius_Lindauer
Engineering model
A. and Morlier, J. (2016) "An improved approach for estimating the hyperparameters of the kriging model for high-dimensional problems through the partial
Surrogate_model
Python library for parallel computing
tasks that are not parallelized within scikit-learn and Incremental Hyperparameter Optimization for scaling hyper-parameter search and parallelized estimators
Dask_(software)
algorithms to easily swap functions of varying complexity. In typical machine learning algorithms, these functions produce a scalar output. Recent development
Kernel methods for vector output
Kernel_methods_for_vector_output
Artificial intelligence that plays Go
between AZ and AGZ include: AZ has hard-coded rules for setting search hyperparameters. The neural network is now updated continually. Chess (unlike Go) can
AlphaGo_Zero
Representation in natural language processing
evaluation function, a grid-search algorithm can be utilized to automate hyperparameter optimization.[citation needed] Multiple approaches exists for evaluating
Sentence_embedding
Statistical concept
1 … N , F ( x | θ ) = as above α = shared hyperparameter for component parameters β = shared hyperparameter for mixture weights H ( θ | α ) = prior probability
Mixture_model
Distribution over functions corresponding to an infinitely wide Bayesian neural network
it is used in deep information propagation to characterize whether hyperparameters and architectures will be trainable. It is related to other large width
Neural network Gaussian process
Neural_network_Gaussian_process
Family of computer vision models designed for efficient inference on mobile devices
significantly reduces computational cost. The MobileNetV1 has two hyperparameters: a width multiplier α {\displaystyle \alpha } that controls the number
MobileNet
Statistical model used in machine learning
A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing
Flow-based_generative_model
Technique for setting initial values of trainable parameters in a neural network
possible. However, a 2013 paper demonstrated that with well-chosen hyperparameters, momentum gradient descent with weight initialization was sufficient
Weight_initialization
Process of reducing the number of random variables under consideration
preserved. CUR matrix approximation Data transformation (statistics) Hyperparameter optimization Information gain in decision trees Johnson–Lindenstrauss
Dimensionality_reduction
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
GPT-2
Automated machine learning system
"Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA". Journal of Machine Learning Research. 18 (25): 1–5 – via jmlr.org. Gijsbers
Auto-WEKA
Topics referred to by the same term
involved in the Hippo signaling pathway Hyperparameter optimization, a technique used in automated machine learning This disambiguation page lists articles
HPO
Large language model by Meta AI
2025-04-10. Peters, Jay; Vincent, James (24 February 2023). "Meta has a new machine learning language model to remind you it does AI too". The Verge. "Meta and
Llama_(language_model)
Concept in information theory
probability distribution p is a concept widely used in information theory, machine learning, and statistical modeling. It is defined as P P ( p ) = ∏ x p ( x )
Perplexity
Non-parametric classification method
distinct. A good k can be selected by various heuristic techniques (see hyperparameter optimization). The special case where the class is predicted to be the
K-nearest_neighbors_algorithm
Multidisciplinary field of science
demanding techniques such as machine learning. Optimizing model performance through careful data partitioning and hyperparameter tuning is essential but requires
Digital_phenotyping
Machine learning model for vision processing
(3x3 to 7x7). ViT is more sensitive to the choice of the optimizer, hyperparameters, and network depth. Preprocessing with a layer of smaller-size, overlapping
Vision_transformer
Models used to produce word embeddings
the models per se, but of the choice of specific hyperparameters. Transferring these hyperparameters to more 'traditional' approaches yields similar performances
Word2vec
Diffusion model over latent embedding space
shape ( 4 , 64 , 64 ) {\displaystyle (4,64,64)} , where 0.18215 is a hyperparameter, which the original authors picked to roughly whiten the encoded vector
Latent_diffusion_model
Statistical analysis technique
are often employed to find solutions. Note also that SPCA introduces hyperparameters quantifying in what capacity large parameter values are penalized.
Sparse_PCA
Architectural motif in neural networks for aggregating information
(x|f,s)} where w ∈ [ 0 , 1 ] {\displaystyle w\in [0,1]} is either a hyperparameter, a learnable parameter, or randomly sampled anew every time. Lp Pooling
Pooling_layer
Family of computer vision models
image approximately 2 ϕ 0 {\displaystyle 2^{\phi _{0}}} times. The hyperparameters α {\displaystyle \alpha } , β {\displaystyle \beta } , and γ {\displaystyle
EfficientNet
testing the robustness and efficiency of algorithms in tasks such as hyperparameter tuning, neural network training, and constrained optimization. Griewank
Griewank_function
Multi-language machine learning library
framework allows developers to track, debug, save checkpoints, modify hyperparameters, and perform early stopping. MXNet supports Python, R, Scala, Clojure
Apache_MXNet
Technique for the generative modeling of a discrete probability distribution
In machine learning, discrete diffusion models are a class of diffusion models, which themselves are a class of latent variable generative models. Each
Discrete_diffusion_model
Machine learning optimization algorithm
Sharpness Aware Minimization (SAM) is an optimization algorithm used in machine learning that aims to improve model generalization. The method seeks to find
Sharpness_aware_minimization
Volume rendering technique
than previous point-based approaches. May require hyperparameter tuning (e.g., reducing position learning rate) for very large scenes. Peak GPU memory consumption
Gaussian_splatting
Method of statistical inference
This may be a vector of parameters. α {\displaystyle \alpha } , the hyperparameter of the parameter distribution, i.e., θ ∼ p ( θ ∣ α ) {\displaystyle
Bayesian_inference
Competitive algorithm for searching a problem space
optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, and causal inference. In a genetic algorithm, a population
Genetic_algorithm
HYPERPARAMETER MACHINE-LEARNING
HYPERPARAMETER MACHINE-LEARNING
Female
Scottish
Feminine form of Scottish Lachlan, LACHINA means "lake-land."
Male
English
Pet form of English Sacheverell, SACHIE means "roe-buck leap."
Female
Yiddish
(×™Ö·×—Ö°× Ö¶×¢) Yiddish form of Hebrew Yochana, YACHNE means "God is gracious."Â
Male
Scottish
Pet form of Scottish Gaelic Lachlann, LACHIE means "lake-land."
Female
French
French feminine form of Latin Martinus, MARTINE means "of/like Mars."Â
Female
English
Variant spelling of English Maureen, MAURINE means "obstinacy, rebelliousness" or "their rebellion."
Male
Hebrew
Variant spelling of Hebrew Yakiyn, YACHIN means "he establishes" or "whom God strengthens."Â
Boy/Male
American, Australian
Weighing Machine
Female
English
Feminine form of English Max, MAXINE means either "the greatest rival" or "the stream of Mack."Â
Male
English
Variant spelling of English unisex Macey, MACIE means "gift of God."
Surname or Lastname
English
English : variant spelling of Machen.Spanish (MachÃn) : probably a nickname from machÃn ‘boor’, ‘lout’, often applied to a blacksmith’s apprentice.French : nickname from Old French machin ‘scheming’.
Female
German
German form of Scottish Malvina, MALWINE means "smooth-brow."
Female
Hawaiian
Hawaiian name MAHINA means "moon; moonlight."
Female
French
Feminine form of French Marin, MARINE means "of the sea."
Girl/Female
Bengali, Indian
Machine
Female
Native American
Native American Hopi name KACHINA means "sacred dancer; spirit."
Surname or Lastname
English
English : occupational name for a stonemason, Anglo-Norman French machun, a Norman dialect variant of Old French masson (see Mason).
Male
Hindi/Indian
(सचिन) Hindi myth name borne by Indra, SACHIN means "pure."
Girl/Female
Australian, Japanese
Child of Machi
Male
French
French form of Latin Macarius, MACAIRE means "blessed."
HYPERPARAMETER MACHINE-LEARNING
HYPERPARAMETER MACHINE-LEARNING
Boy/Male
Hindu, Indian, Punjabi, Sanskrit, Sikh
Divine Rama
Girl/Female
American, British, English
Crowned with Laurels; Modern Variant of Lora and Laurie Referring to the Laurel Tree; Sweet Bay Tree Symbolic of Honor and Victory
Girl/Female
Indian
Women God
Girl/Female
Gujarati, Hindu, Indian, Marathi
Desire; Love
Boy/Male
Tamil
Well defined body
Girl/Female
Muslim
Courage, Successor, Help
Male
Portuguese
Portuguese form of Latin Reynaldus, RONALDO means "wise ruler."
Boy/Male
Arabic, Hindu, Indian, Muslim, Punjabi, Sikh, Tamil
Symbol
Boy/Male
Hindu, Indian, Tamil
Faces of Lord Muruga
Girl/Female
Arabic, Australian, French, Muslim
Arrow
HYPERPARAMETER MACHINE-LEARNING
HYPERPARAMETER MACHINE-LEARNING
HYPERPARAMETER MACHINE-LEARNING
HYPERPARAMETER MACHINE-LEARNING
HYPERPARAMETER MACHINE-LEARNING
v. t.
To wind marline around; as, to marline a rope.
a.
Of or pertaining to the sea; having to do with the ocean, or with navigation or naval affairs; nautical; as, marine productions or bodies; marine shells; a marine engine.
imp. & p. p.
of Machine
a.
Of or pertaining to machines.
v. t.
To subject to the action of machinery; to effect by aid of machinery; to print with a printing machine.
n.
The working parts of a machine, engine, or instrument; as, the machinery of a watch.
a.
A picture representing some marine subject.
n.
Machines, in general, or collectively.
a.
Of or pertaining to cows; pertaining to, derived from, or caused by, vaccinia; as, vaccine virus; the vaccine disease.
n.
A combination of persons acting together for a common purpose, with the agencies which they use; as, the social machine.
n.
Supernatural agency in a poem, or a superhuman being introduced to perform some exploit.
n.
Any one of numerous species of Diptera belonging to Tachina and allied genera. Their larvae are external parasites of other insects.
pl.
of Tachina
n.
In general, any combination of bodies so connected that their relative motions are constrained, and by means of which force and motion may be transmitted and modified, as a screw and its nut, or a lever arranged to turn about a fulcrum or a pulley about its pivot, etc.; especially, a construction, more or less complex, consisting of a combination of moving parts, or simple mechanical elements, as wheels, levers, cams, etc., with their supports and connecting framework, calculated to constitute a prime mover, or to receive force and motion from a prime mover or from another machine, and transmit, modify, and apply them to the production of some desired mechanical effect or work, as weaving by a loom, or the excitation of electricity by an electrical machine.
a.
Formed by the action of the currents or waves of the sea; as, marine deposits.
v. t.
To contrive, as a plot; to plot; as, to machinate evil.
n.
One who or operates a machine; a machinist.
n.
A political organization arranged and controlled by one or more leaders for selfish, private or partisan ends.