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Class of statistical modeling methods
Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured
Conditional_random_field
Set of random variables
physics and probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having a Markov property
Markov_random_field
Type of database that uses vectors to represent other data
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
Vector_database
Overview of and topical guide to machine learning
machines Random Forests Ensembles of classifiers Bootstrap aggregating (bagging) Boosting (meta-algorithm) Ordinal classification Conditional Random Field ANOVA
Outline_of_machine_learning
2018 text-generating language model
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
GPT-1
Mathematical function
Markov random field (MRF), Gibbs random field, conditional random field (CRF), and Gaussian random field. In 1974, Julian Besag proposed an approximation
Random_field
Supervised machine learning techniques
Probabilistic Soft Logic, and constrained conditional models. The main techniques are: Conditional random fields Structured support vector machines Structured
Structured_prediction
Deep learning architecture
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
Mamba (deep learning architecture)
Mamba_(deep_learning_architecture)
Machine learning technique
Tzyy-Ping (27 June 2017). "Improving EEG-Based Emotion Classification Using Conditional Transfer Learning". Frontiers in Human Neuroscience. 11 334. doi:10.3389/fnhum
Transfer_learning
Type of machine learning model
Interventions for Mental Health Problems: Systematic Review and Meta-analysis of Randomized Controlled Trials". Journal of Medical Internet Research. 25 e43862. doi:10
Large_language_model
Probability theory and statistics concept
event. Given two jointly distributed random variables X {\displaystyle X} and Y {\displaystyle Y} , the conditional probability distribution of Y {\displaystyle
Conditional probability distribution
Conditional_probability_distribution
Type of large language model
(USPTO) to seek domestic trademark registration for the term "GPT" in the field of AI. OpenAI sought to expedite handling of its application, but the USPTO
Generative pre-trained transformer
Generative_pre-trained_transformer
Machine learning technique
auto-regressively generate the corresponding response y {\displaystyle y} when given a random prompt x {\displaystyle x} . The original paper recommends to SFT for only
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Similarity measure for number sequences
products between two random unit vectors in RD". CrossValidated. Graham L. Giller (2012). "The Statistical Properties of Random Bitstreams and the Sampling
Cosine_similarity
Statistical model for a binary dependent variable
predict the likelihood of a homeowner defaulting on a mortgage. Conditional random fields, an extension of logistic regression to sequential data, are used
Logistic_regression
Optimization algorithm
Kleeman, Christopher D. Manning (2008). Efficient, Feature-based, Conditional Random Field Parsing. Proc. Annual Meeting of the ACL. LeCun, Yann A., et al
Stochastic_gradient_descent
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
Subset of artificial intelligence
probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example
Machine_learning
Software user interface
correct decisions in building a model. HITL improves machine learning over random sampling by selecting the most critical data needed to refine the model
Human-in-the-loop
Machine-learning and computational-neuroscience conference
evaluate randomness in the reviewing process. Several researchers interpreted the result. Regarding whether the decision in NIPS is completely random or not
Conference on Neural Information Processing Systems
Conference_on_Neural_Information_Processing_Systems
French computer scientist (born 1960)
methods, and the Graph Transformer Networks method (similar to conditional random field), which he applied to handwriting recognition and Optical character
Yann_LeCun
Class of algorithms for pattern analysis
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
Kernel_method
Academic conference in machine learning
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
International Conference on Machine Learning
International_Conference_on_Machine_Learning
Integrated circuit technology
distributed processing across small computing elements. This interdisciplinary field integrates biology, physics, mathematics, computer science, and electronic
Neuromorphic_computing
Machine learning methods using multiple input modalities
Cross-modal retrieval Vision-language model Hopfield network Markov random field Markov chain Monte Carlo SGLang Hendriksen, Mariya; Bleeker, Maurits;
Multimodal_learning
researcher in machine learning. He is best known for proposing the Conditional Random Fields with Andrew McCallum and Fernando C.N. Pereira. In 2017, Lafferty
John_D._Lafferty
Statistical method
Random sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers
Random_sample_consensus
Type of feedforward neural network
multilayered perceptron model, consisting of an input layer, a hidden layer with randomized weights that did not learn, and an output layer with learnable connections
Multilayer_perceptron
Machine learning paradigm
{\displaystyle X=\left\{x_{1},\ldots x_{N}\right\}} of N {\displaystyle N} random samples containing one positive sample from p ( x t + k ∣ c t ) {\displaystyle
Self-supervised_learning
Memory unit used in neural networks
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
Gated_recurrent_unit
Technique for the generative modeling of a continuous probability distribution
random image from ImageNet. To generate images from just one category, one would need to impose the condition, and then sample from the conditional distribution
Diffusion_model
Type of convolutional neural network
the GPU memory. Recently, there had also been an interest in receptive field based U-Net models for medical image segmentation. The network consists
U-Net
Model-free reinforcement learning algorithm
beneficial will have the highest probability of being selected from the random sample. After an agent arrives at a different scenario (a new state) by
Proximal_policy_optimization
Statistical model of language
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
Language_model
Method used to normalize the range of independent variables
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
Feature_scaling
Field of machine learning
expected return, a risk-measure of the return is optimized, such as the conditional value at risk (CVaR). In addition to mitigating risk, the CVaR objective
Reinforcement_learning
Deep neural network for generating raw audio
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
WaveNet
Vector quantization algorithm minimizing the sum of squared deviations
Forgy and Random Partition. The Forgy method randomly chooses k observations from the dataset and uses these as the initial means. The Random Partition
K-means_clustering
Numerical method that reduces the complexity of computationally intensive simulations
turbulences, is to decompose a random vector field u(x, t) into a set of deterministic spatial functions Φk(x) modulated by random time coefficients ak(t) so
Proper orthogonal decomposition
Proper_orthogonal_decomposition
Mathematical model used for classification or regression
Types of discriminative models include logistic regression (LR), conditional random fields (CRFs), decision trees among many others. Unlike generative modelling
Discriminative_model
Probabilistic model
probabilistic model for which a graph expresses the conditional dependence structure between random variables. Graphical models are commonly used in probability
Graphical_model
Academic conference in machine learning
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
International Conference on Learning Representations
International_Conference_on_Learning_Representations
Set of methods for supervised statistical learning
given pair of random variables X , y {\displaystyle X,\,y} . In particular, let y x {\displaystyle y_{x}} denote y {\displaystyle y} conditional on the event
Support_vector_machine
Conversational software
security threats. Chatbots have shown to be an emerging technology used in the field of mental health. Its usage may encourage users to seek advice on matters
Chatbot
Statistical Markov model
discriminative model is the linear-chain conditional random field. This uses an undirected graphical model (aka Markov random field) rather than the directed graphical
Hidden_Markov_model
Reverse-engineering neural networks
characterize individual features and circuits within models, while the broader field tended towards gradient-based approaches like saliency maps. Before circuit
Mechanistic_interpretability
Algorithm for anomaly detection
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
Local_outlier_factor
theorem Random field Conditional random field Borel–Cantelli lemma Wick product Conditioning (probability) Conditional expectation Conditional probability
List_of_probability_topics
Machine learning calibration technique
well-calibrated models such as logistic regression, multilayer perceptrons, and random forests. An alternative approach to probability calibration is to fit an
Platt_scaling
Concept in machine learning
Non-independent and identically distributed random (non-IID) data Time leakage (for example, splitting a time-series dataset randomly instead of newer data in test
Leakage_(machine_learning)
Algorithm for modelling sequential data
optimized for representation learning, autoregressive generation, or conditional sequence-to-sequence tasks. The original version of the transformer architecture
Transformer_(deep_learning)
Extracting features from raw data for machine learning
the principles of feature engineering are applied in various scientific fields, including physics. For example, physicists construct dimensionless numbers
Feature_engineering
Software program
The DeepDream model has also been demonstrated to have application in the field of art history. DeepDream was used for Foster the People's music video for
DeepDream
3D reconstruction technique
A neural radiance field (NeRF) is a neural field for reconstructing a three-dimensional representation of a scene from two-dimensional images. The NeRF
Neural_radiance_field
American computer scientist
with John D. Lafferty and Fernando Pereira, McCallum developed conditional random fields, first described in a paper presented at the International Conference
Andrew_McCallum
AI platform developed by IBM
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
IBM_Watsonx
Deep learning library
Executes all calculations on the GPU # Create a tensor and fill it with random numbers a = torch.randn(2, 3, device=device, dtype=dtype) print(a) # Output:
PyTorch
Mathematical theorem
End of Proof Markov random field Conditional random field Lafferty, John D.; Mccallum, Andrew (2001). "Conditional Random Fields: Probabilistic Models
Hammersley–Clifford_theorem
Algorithm for supervised learning of binary classifiers
experimented with. The S-units are connected to the A-units randomly (according to a table of random numbers) via a plugboard (see photo), to "eliminate any
Perceptron
Models used to produce word embeddings
with hierarchical softmax and/or negative sampling. To approximate the conditional log-likelihood a model seeks to maximize, the hierarchical softmax method
Word2vec
Extraction of named entity mentions in unstructured text into pre-defined categories
classifier types have been used to perform machine-learned NER, with conditional random fields being a typical choice. Transformers features token classification
Named-entity_recognition
Optimization algorithm for artificial neural networks
{\displaystyle x_{2}} , will compute an output y that likely differs from t (given random weights). A loss function L ( t , y ) {\displaystyle L(t,y)} is used for
Backpropagation
Method in natural language processing
the introduction of latent semantic analysis in the late 1980s and the random indexing approach for collecting word co-occurrence contexts. In 2000, Bengio
Word_embedding
Machine learning technique
applications in running the largest models, as a simple way to perform conditional computation: only parts of the model are used, the parts chosen according
Mixture_of_experts
Machine learning software library
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
MindSpore
Method of data analysis
directions through the data (or two of the original variables) are chosen at random, the clusters may be much less spread apart from each other, and may in
Principal_component_analysis
Smooth approximation of one-hot arg max
entropy; it is "more random"), while a lower temperature results in a sharper output distribution, with one value dominating. In some fields, the base is fixed
Softmax_function
Measurable property or characteristic
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
Feature_(machine_learning)
common models in use are the maximum entropy Markov model and conditional random field. Artificial intelligence Bayesian networks (of which HMMs are an
Sequence_labeling
Framework for mathematical analysis of machine learning
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
Probably approximately correct learning
Probably_approximately_correct_learning
random variable. Several kinds of random fields exist, among them the Markov random field (MRF), Gibbs random field (GRF), conditional random field (CRF)
Random_element
Recurrent neural network architecture
Hochreiter, Heuesel, and Obermayr applied LSTM to protein homology detection the field of biology. 2009: Justin Bayer et al. introduced neural architecture search
Long_short-term_memory
Process of automating the application of machine learning
numerical, or text Column intent detection; e.g., target/label, stratification field, numerical feature, categorical text feature, or free text feature Task
Automated_machine_learning
Computer programming concept
Carlo RL algorithms. The TD algorithm has also received attention in the field of neuroscience. Researchers discovered that the firing rate of dopamine
Temporal_difference_learning
Deep learning method
trivially extended to conditional GAN by providing the labels to both the generator and the discriminator. Concretely, the conditional GAN game is just the
Generative adversarial network
Generative_adversarial_network
Tuning parameter (hyperparameter) in optimization
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
Learning_rate
Class of artificial neural network
process arbitrary sequences of inputs. An RNN can be trained into a conditionally generative model of sequences, aka autoregression. Concretely, let us
Recurrent_neural_network
Data analysis technique
Luo et al. observed that useful EEG signal data could be generated by Conditional Wasserstein Generative Adversarial Networks (GANs) which was then introduced
Data_augmentation
Method in machine learning
between statistical variables in a dataset. This makes random forests particularly useful in such fields as banking, healthcare, the stock market, and e-commerce
Bootstrap_aggregating
2025 multimodal model by OpenAI
OpenAI is going to bring it back". TechRadar. Retrieved August 9, 2025. Field, Hayden (August 13, 2025). "OpenAI will update GPT-5's "personality" after
GPT-5
Machine learning problem
generalize this notion of classifiers: instead of functions, they are conditional distributions Pr ( Y | X ) {\displaystyle \Pr(Y\vert X)} , meaning that
Probabilistic_classification
Framework for machine learning
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals
Statistical_learning_theory
Automated recognition of patterns and regularities in data
component analysis (ICA) Principal components analysis (PCA) Conditional random fields (CRFs) Hidden Markov models (HMMs) Maximum entropy Markov models
Pattern_recognition
Machine learning strategy
concepts from the field of machine learning (e.g. conflict and ignorance) with adaptive, incremental learning policies in the field of online machine
Active learning (machine learning)
Active_learning_(machine_learning)
Type of artificial neural network
multilayered perceptron model, consisting of an input layer, a hidden layer with randomized weights that did not learn, and an output layer with learnable connections
Feedforward_neural_network
Method of improving artificial neural network
distribution, which shifts during training due to two main factors: the random starting values of the network’s settings (parameter initialization) and
Batch_normalization
Machine learning technique
resulting algorithm is called gradient-boosted trees; it usually outperforms random forest. As with other boosting methods, a gradient-boosted trees model is
Gradient_boosting
Technique in machine learning
Amr; Abdine, Hadi; Shang, Guokan; Vazirgiannis, Michalis (2025). "Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning"
Curriculum_learning
Technique for setting initial values of trainable parameters in a neural network
gradient is likely nonzero at initialization, avoiding the dying ReLU problem. Random initialization means sampling the weights from a normal distribution or
Weight_initialization
Type of activation function
Kadmon, Jonathan; Sompolinsky, Haim (2015-11-19). "Transition to Chaos in Random Neuronal Networks". Physical Review X. 5 (4) 041030. arXiv:1508.06486. Bibcode:2015PhRvX
Rectified_linear_unit
Topics referred to by the same term
rangefinder Case report form, a document in clinical trial research Conditional random field, in machine learning, a type of graphical model Constant Rate Factor
CRF
Ensemble learning method
learner is defined as a classifier that performs only slightly better than random guessing, whereas a strong learner is a classifier that is highly correlated
Boosting_(machine_learning)
Type of artificial intelligence system
when the weights of the other modules of the block are still untrained and random. As training progresses, their values gradually increase. These gates have
Vision–language_model
Type of feedforward neural network
Borovykh, Anastasia; Bohte, Sander; Oosterlee, Cornelis W. (2018-09-17). "Conditional Time Series Forecasting with Convolutional Neural Networks". arXiv:1703
Convolutional_neural_network
Adaptive boosting based classification algorithm
weak, but as long as the performance of each one is slightly better than random guessing, the final model can be proven to converge to a strong learner
AdaBoost
2023 text-generating language model
Technica. Archived from the original on May 3, 2023. Retrieved May 3, 2023. Field, Hayden (May 13, 2024). "OpenAI launches new AI model and desktop version
GPT-4
French mathematician and computer scientist
learning methods, such as Graph Transformer Networks (similar to conditional random field), and applied them to handwriting recognition and OCR. The bank
Léon_Bottou
2023 text-generating language model
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
IBM_Granite
2019 text-generating language model
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
GPT-2
AI that learns decision rules from data
PGD t-SNE SDL Structured prediction Graphical models Bayes net Conditional random field Hidden Markov Anomaly detection RANSAC k-NN Local outlier factor
Rule-based_machine_learning
CONDITIONAL RANDOM-FIELD
CONDITIONAL RANDOM-FIELD
Male
Norwegian
 Norwegian form of Old Norse Arnþórr, ANDOR means "eagle of Thor." Compare with another form of Andor.
Surname or Lastname
English (chiefly East Anglia)
English (chiefly East Anglia) : patronymic from the Middle English personal name Rand(e) (see Rand 1).
Female
English
Short form of English Miranda, RANDA means "worthy of admiration."Â
Male
English
Pet form of English Randall and Randolph, both RANDY means "shield-wolf." Compare with feminine Randy.
Boy/Male
English
Son of Rand.
Surname or Lastname
English
English : variant of Ransom.
Male
English
 Variant spelling of Middle English Randulf, RANDOLF means "shield-wolf." Compare with other forms of Randolf.
Male
English
Medieval form of English Randolf, RANDAL means "shield-wolf."
Surname or Lastname
English
English : variant spelling of Randall.Americanized spelling of Randel.
Surname or Lastname
English
English : variant of Brandon.
Boy/Male
English American
Son of Rand.
Surname or Lastname
English
English : unexplained; perhaps a variant of Francom.
Surname or Lastname
English
English : probably a variant of Crandon, a habitational name from Crandon in Somerset or Crandean in Falmer, Sussex. Compare Grandin.
Male
Hungarian
 Variant spelling of Hungarian András, ANDOR means "man; warrior." Compare with another form of Andor.
Surname or Lastname
English or Scottish
English or Scottish : unexplained. Possibly, as Black suggests, a reduced form of Langdon.French : from the old Germanic personal name element Lando (see Land), via the oblique case, Landonis.
Female
English
Variant spelling of English Randy, RANDI means "worthy of admiration."
Surname or Lastname
English
English : variant of Rand 1, from the Old French oblique case.
Male
Scandinavian
 Scandinavian form of Old Norse Randolfr, RANDOLF means "shield-wolf." Compare with another form of Randolf.
Surname or Lastname
English
English : patronymic from Rand 1.
Female
English
Pet form of English Miranda, RANDY means "worthy of admiration."Â Compare with masculine Randy.Â
CONDITIONAL RANDOM-FIELD
CONDITIONAL RANDOM-FIELD
Girl/Female
British, English
Bright; Famous
Girl/Female
French, German, Hebrew
Bitterness
Girl/Female
Hindu, Indian, Tamil
Truth of the World
Boy/Male
Tamil
Pandurangan | பநà¯à®¤à¯à®°à®‚கந
A deity, One with pale white complexion, Lord Vishnu
Boy/Male
Hindu
Rain
Female
English
English variant spelling of Danish/Swedish Greta, GRETTA means "pearl."
Boy/Male
Arabic, Hindu, Indian, Muslim
God Blessings
Boy/Male
Arabic, Muslim
Brightness of the Faith
Boy/Male
Arabic
Justice
Boy/Male
Tamil
CONDITIONAL RANDOM-FIELD
CONDITIONAL RANDOM-FIELD
CONDITIONAL RANDOM-FIELD
CONDITIONAL RANDOM-FIELD
CONDITIONAL RANDOM-FIELD
n.
To invest with, or limit by, conditions; to burden or qualify by a condition; to impose or be imposed as the condition of.
adv.
Conditionally.
adv.
In a conditional manner; subject to a condition or conditions; not absolutely or positively.
a.
Going at random or by chance; done or made at hazard, or without settled direction, aim, or purpose; hazarded without previous calculation; left to chance; haphazard; as, a random guess.
v. t.
To put under conditions; to render conditional.
v. i.
To go or stray at random.
n.
Random.
n.
To exact a ransom for, or a payment on.
n.
A roving motion; course without definite direction; want of direction, rule, or method; hazard; chance; -- commonly used in the phrase at random, that is, without a settled point of direction; at hazard.
a.
Unconditional.
a.
Surrounded; circumstanced; in a certain state or condition, as of property or health; as, a well conditioned man.
a.
Not conditional limited, or conditioned; made without condition; absolute; unreserved; as, an unconditional surrender.
a.
Expressing a condition or supposition; as, a conditional word, mode, or tense.
a.
Containing, implying, or depending on, a condition or conditions; not absolute; made or granted on certain terms; as, a conditional promise.
adv.
In a random manner.
n.
A conditional word, mode, or proposition.
v. t.
Conditional.
n.
Distance to which a missile is cast; range; reach; as, the random of a rifle ball.
a.
Not conditioned or subject to conditions; unconditional.