Acronyms: Difference between revisions

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{{see also|Guides|Terms|Abbreviations}}
{{see also|Guides|Terms|Abbreviations}}
{|
{|
|-
| '''[[A*]]''' ||  || [[A* Search Algorithm]]
|-
| '''[[A/B Testing]]''' ||  || [[A statistical method for comparing two or more treatments or algorithms]]
|-
| '''[[A3C]]''' ||  || [[Asynchronous Advantage Actor-Critic]]
|-
| '''[[ABAC]]''' ||  || [[Attribute-Based Access Control]]
|-
|-
| '''[[ACE]]''' ||  || [[Alternating conditional expectation algorithm]]
| '''[[ACE]]''' ||  || [[Alternating conditional expectation algorithm]]
|-
|-
| '''[[ADT]]''' ||  || [[Automatic Drum Transcription]]
| '''[[ACO]]''' ||  || [[Ant Colony Optimization]]
|-
|-
| '''[[AdA]]''' ||  || [[Adaptive Agent]]
| '''[[AdA]]''' ||  || [[Adaptive Agent]]
|-
| '''[[Adam]]''' ||  || [[Adaptive Moment Estimation]]
|-
| '''[[ADASYN]]''' ||  || [[Adaptive Synthetic Sampling]]
|-
| '''[[ADT]]''' ||  || [[Automatic Drum Transcription]]
|-
|-
| '''[[AE]]''' ||  || [[Autoencoder]]
| '''[[AE]]''' ||  || [[Autoencoder]]
|-
| '''[[AGC]]''' ||  || [[Adaptive Gradient Clipping]]
|-
|-
| '''[[AGI]]''' ||  || [[Artificial general intelligence]]
| '''[[AGI]]''' ||  || [[Artificial general intelligence]]
|-
|-
| '''[[AI]]''' ||  || [[Artificial intelligence]]
| '''[[AI]]''' ||  || [[Artificial intelligence]]
|-
| '''[[AIaaS]]''' ||  || [[Artificial Intelligence as a Service]]
|-
|-
| '''[[AIWPSO]]''' ||  || [[Adaptive Inertia Weight Particle Swarm Optimization]]
| '''[[AIWPSO]]''' ||  || [[Adaptive Inertia Weight Particle Swarm Optimization]]
|-
| '''[[AL]]''' ||  || [[Active Learning]]
|-
|-
| '''[[AM]]''' ||  || [[Activation maximization]]
| '''[[AM]]''' ||  || [[Activation maximization]]
|-
| '''[[AMR]]''' ||  || [[Abstract Meaning Representation]]
|-
|-
| '''[[AMT]]''' ||  || [[Automatic Music Transcription]]
| '''[[AMT]]''' ||  || [[Automatic Music Transcription]]
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| '''[[ANOVA]]''' ||  || [[Analysis of variance]]
| '''[[ANOVA]]''' ||  || [[Analysis of variance]]
|-
|-
| '''[[AR]]''' || || [[Augmented reality]]
| '''[[API]]''' ||  || [[Application Programming Interface]]
|-
| '''[[AR]]''' ||   || [[Augmented reality]]
|-
| '''[[ARNN]]''' ||  || [[Anticipation Recurrent Neural Network]]
|-
|-
| '''[[ASI]]''' ||  || [[Artificial superintelligence]]
| '''[[ASI]]''' ||  || [[Artificial superintelligence]]
|-
| '''[[ASIC]]''' ||  || [[Application-Specific Integrated Circuit]]
|-
|-
| '''[[ASR]]''' ||  || [[Automatic speech recognition]]
| '''[[ASR]]''' ||  || [[Automatic speech recognition]]
|-
| '''[[AST]]''' ||  || [[Automated speech translation]]
|-
|-
| '''[[AUC]]''' ||  || [[Area Under the Curve]]
| '''[[AUC]]''' ||  || [[Area Under the Curve]]
|-
| '''[[AutoML]]''' ||  || [[Automated Machine Learning]]
|-
| '''[[BB84]]''' ||  || [[A quantum key distribution protocol (named after its inventors, Bennett and Brassard, and the year 1984)]]
|-
| '''[[BBO]]''' ||  || [[Biogeography-Based Optimization]]
|-
|-
| '''[[BCE]]''' ||  || [[Binary cross-entropy]]
| '''[[BCE]]''' ||  || [[Binary cross-entropy]]
|-
| '''[[BDT]]''' ||  || [[Boosted Decision Tree]]
|-
|-
| '''[[BERT]]''' ||  || [[Bidirectional Encoder Representations from Transformers]]
| '''[[BERT]]''' ||  || [[Bidirectional Encoder Representations from Transformers]]
|-
| '''[[BFS]]''' ||  || [[Breadth-First Search]]
|-
| '''[[BI]]''' ||  || [[Business Intelligence]]
|-
| '''[[BiFPN]]''' ||  || [[Bidirectional Feature Pyramid Network]]
|-
| '''[[BILSTM]]''' ||  || [[Bidirectional Long Short-Term Memory]]
|-
|-
| '''[[BLEU]]''' ||  || [[Bilingual evaluation understudy]]
| '''[[BLEU]]''' ||  || [[Bilingual evaluation understudy]]
|-
|-
| '''[[BP]]''' ||  || [[Backpropagation]]
| '''[[BN]]''' ||  || [[Bayesian Network]]
|-
| '''[[BNN]]''' ||  || [[Bayesian Neural Network]]
|-
| '''[[BO]]''' ||  || [[Bayesian Optimization]]
|-
| '''[[BP]]''' ||  || [[Backpropagation]]
|-
| '''[[BPE]]''' ||  || [[Byte Pair Encoding]]
|-
| '''[[BPMF]]''' ||  || [[Bayesian Probabilistic Matrix Factorization]]
|-
| '''[[BPN]]''' ||  || [[Backpropagation Neural Network]]
|-
| '''[[BPTT]]''' ||  || [[Backpropagation through time]]
|-
| '''[[BQML]]''' ||  || [[Big Query Machine Learning]]
|-
| '''[[BR]]''' ||  || [[Best-Response (in game theory)]]
|-
| '''[[BRDF]]''' ||  || [[Bidirectional reflectance distribution function]]
|-
| '''[[BRNN]]''' ||  || [[Bidirectional Recurrent Neural Network]]
|-
| '''[[BRR]]''' ||  || [[Bayesian ridge regression]]
|-
| '''[[CAD]]''' ||  || [[Computer-Aided Design]]
|-
| '''[[CAE]]''' ||  || [[Contractive Autoencoder]]
|-
| '''[[CALA]]''' ||  || [[Continuous Action-set Learning Automata]]
|-
| '''[[CAM]]''' ||  || [[Computer-Aided Manufacturing]]
|-
| '''[[CAPTCHA]]''' ||  || [[Completely Automated Public Turing test to tell Computers and Humans Apart]]
|-
| '''[[CART]]''' ||  || [[Classification And Regression Tree]]
|-
| '''[[CASE]]''' ||  || [[Computer-Aided Software Engineering]]
|-
| '''[[CatBoost]]''' ||  || [[Categorical Boosting]]
|-
| '''[[CAV]]''' ||  || [[Concept Activation Vectors]]
|-
| '''[[CBAC]]''' ||  || [[Content-Based Access Control]]
|-
| '''[[CBI]]''' ||  || [[Counterfactual Bias Insertion]]
|-
| '''[[CBOW]]''' ||  || [[Continuous Bag of Words]]
|-
| '''[[CBR]]''' ||  || [[Case-Based Reasoning]]
|-
| '''[[CCA]]''' ||  || [[Canonical Correlation Analysis]]
|-
| '''[[CCC]]''' ||  || [[Canonical Correlation Coefficients]]
|-
|-
| '''[[BPTT]]''' || || [[Backpropagation through time]]
| '''[[CCE]]''' ||   || [[Categorical cross-entropy]]
|-
|-
| '''[[BRNN]]''' || || [[Bidirectional Recurrent Neural Network]]
| '''[[CDBN]]''' ||   || [[Convolutional Deep Belief Networks]]
|-
|-
| '''[[BRR]]''' || || [[Bayesian ridge regression]]
| '''[[CE]]''' ||   || [[Cross-Entropy]]
|-
|-
| '''[[CAE]]''' || || [[Contractive Autoencoder]]
| '''[[CEC]]''' ||   || [[Constant Error Carousel]]
|-
|-
| '''[[CBOW]]''' || || [[Continuous Bag of Words]]
| '''[[CEGAR]]''' ||   || [[Counterexample-Guided Abstraction Refinement]]
|-
|-
| '''[[CCE]]''' || || [[Categorical cross-entropy]]
| '''[[CEGIS]]''' ||   || [[Counterexample-Guided Inductive Synthesis]]
|-
| '''[[CF]]''' ||  || [[Common Features]]
|-
| '''[[cGAN]]''' ||  || [[Conditional Generative Adversarial Network]]
|-
|-
| '''[[CL]]''' ||  || [[Confident learning]]
| '''[[CL]]''' ||  || [[Confident learning]]
|-
|-
| '''[[CLIP]]''' ||  || [[Contrastive Language-Image Pre-Training]]
| '''[[CLIP]]''' ||  || [[Contrastive Language-Image Pre-Training]]
|-
| '''[[CLNN]]''' ||  || [[ConditionaL Neural Networks]]
|-
| '''[[CMA]]''' ||  || [[Covariance Matrix Adaptation]]
|-
| '''[[CMA-ES]]''' ||  || [[Covariance Matrix Adaptation Evolution Strategy]]
|-
| '''[[CMAC]]''' ||  || [[Cerebellar Model Articulation Controller]]
|-
| '''[[CMMs]]''' ||  || [[Conditional Markov Model]]
|-
|-
| '''[[CNN]]''' ||  || [[Convolutional neural network]]
| '''[[CNN]]''' ||  || [[Convolutional neural network]]
|-
| '''[[COIN-OR]]''' ||  || [[Computational Infrastructure for Operations Research]]
|-
| '''[[ConvNet]]''' ||  || [[Convolutional Neural Network]]
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| '''[[COT]]''' ||  || [[Chain of Thought]]
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| '''[[COTE]]''' ||  || [[Collective of Transformation-Based Ensembles]]
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| '''[[COTP]]''' ||  || [[Chain of Thought Prompting]]
|-
| '''[[CP]]''' ||  || [[Constraint Programming]]
|-
| '''[[CPLEX]]''' ||  || [[An optimization solver (from "C" programming language and "simplex")]]
|-
| '''[[CPN]]''' ||  || [[Colored Petri Nets]]
|-
| '''[[CRBM]]''' ||  || [[Conditional Restricted Boltzmann Machine]]
|-
| '''[[CRF]]''' ||  || [[Conditional Random Field]]
|-
| '''[[CRFs]]''' ||  || [[Conditional Random Fields]]
|-
| '''[[CRNN]]''' ||  || [[Convolutional Recurrent Neural Network]]
|-
| '''[[CSLR]]''' ||  || [[Continuous Sign Language Recognition]]
|-
| '''[[CSP]]''' ||  || [[Constraint Satisfaction Problem]]
|-
|-
| '''[[CSV]]''' ||  || [[Comma-separated values]]
| '''[[CSV]]''' ||  || [[Comma-separated values]]
|-
| '''[[CT-LSTM]]''' ||  || [[Convolutional Transformer Long Short-Term Memory]]
|-
| '''[[CTC]]''' ||  || [[Connectionist Temporal Classification]]
|-
| '''[[CTR]]''' ||  || [[Collaborative Topic Regression]]
|-
|-
| '''[[CUDA]]''' ||  || [[Compute Unified Device Architecture]]
| '''[[CUDA]]''' ||  || [[Compute Unified Device Architecture]]
|-
|-
| '''[[CV]]''' ||  || [[Computer Vision]], [[Cross validation]], [[Coefficient of variation]]
| '''[[CV]]''' ||  || [[Computer Vision, Cross validation, Coefficient of variation]]
|-
| '''[[Cyc]]''' ||  || [[CycL and OpenCyc, a knowledge representation and reasoning system]]
|-
| '''[[D*]]''' ||  || [[Dynamic A* Search Algorithm]]
|-
| '''[[DAAF]]''' ||  || [[Data Augmentation and Auxiliary Feature]]
|-
| '''[[DaaS]]''' ||  || [[Data as a Service]]
|-
| '''[[DAE]]''' ||  || [[Denoising AutoEncoder or Deep AutoEncoder]]
|-
| '''[[DAML]]''' ||  || [[DARPA Agent Markup Language]]
|-
| '''[[DART]]''' ||  || [[Disturbance Aware Regression Tree]]
|-
| '''[[DBM]]''' ||  || [[Deep Boltzmann Machine]]
|-
| '''[[DBN]]''' ||  || [[Deep belief network]]
|-
| '''[[DBSCAN]]''' ||  || [[Density-Based Spatial Clustering of Applications with Noise]]
|-
| '''[[DCAI]]''' ||  || [[Data-centric AI]]
|-
| '''[[DCGAN]]''' ||  || [[Deep Convolutional Generative Adversarial Network]]
|-
| '''[[DCMDN]]''' ||  || [[Deep Convolutional Mixture Density Network]]
|-
| '''[[DDPG]]''' ||  || [[Deep Deterministic Policy Gradient]]
|-
| '''[[DE]]''' ||  || [[Differential evolution]]
|-
|-
| '''[[DBN]]''' || || [[Deep belief network]]
| '''[[DeconvNet]]''' ||   || [[DeConvolutional Neural Network]]
|-
|-
| '''[[DCAI]]''' || || [[Data-centric AI]]
| '''[[DeepLIFT]]''' ||   || [[Deep Learning Important FeaTures]]
|-
|-
| '''[[DE]]''' || || [[Differential evolution]]
| '''[[DFS]]''' ||   || [[Depth-First Search]]
|-
|-
| '''[[DL]]''' ||  || [[Deep learning]]
| '''[[DL]]''' ||  || [[Deep learning]]
|-
| '''[[DM]]''' ||  || [[Diffusion model]]
|-
|-
| '''[[DNN]]''' ||  || [[Deep neural network]]
| '''[[DNN]]''' ||  || [[Deep neural network]]
|-
| '''[[DP]]''' ||  || [[Dynamic Programming]]
|-
|-
| '''[[DQN]]''' ||  || [[Deep Q-Learning]]
| '''[[DQN]]''' ||  || [[Deep Q-Learning]]
|-
| '''[[DR]]''' ||  || [[Detection Rate]]
|-
| '''[[DRL]]''' ||  || [[Deep Reinforcement Learning]]
|-
| '''[[DS]]''' ||  || [[Data Science]]
|-
| '''[[DSN]]''' ||  || [[Deep Stacking Network]]
|-
| '''[[DSR]]''' ||  || [[Deep Symbolic Reinforcement Learning]]
|-
| '''[[DSS]]''' ||  || [[Decision Support System]]
|-
| '''[[DSW]]''' ||  || [[Data Stream Warehousing]]
|-
| '''[[DT]]''' ||  || [[Decision Tree]]
|-
| '''[[DTD]]''' ||  || [[Deep Taylor Decomposition]]
|-
| '''[[DWT]]''' ||  || [[Discrete Wavelet Transform]]
|-
|-
| '''[[EDA]]''' ||  || [[Exploratory data analysis]]
| '''[[EDA]]''' ||  || [[Exploratory data analysis]]
|-
|-
| '''[[FN]]''' || || [[False negative]]
| '''[[EKF]]''' ||  || [[Extended Kalman Filter]]
|-
| '''[[ELECTRA]]''' ||  || [[Efficiently Learning an Encoder that Classifies Token Replacements Accurately]]
|-
| '''[[ELM]]''' ||  || [[Extreme Learning Machine]]
|-
| '''[[ELMo]]''' ||  || [[Embeddings from Language Models]]
|-
| '''[[ELU]]''' ||  || [[Exponential Linear Unit]]
|-
| '''[[EM]]''' ||  || [[Expectation maximization]]
|-
| '''[[EMD]]''' ||  || [[Entropy Minimization Discretization]]
|-
| '''[[ERNIE]]''' ||  || [[Enhanced Representation through kNowledge IntEgration]]
|-
| '''[[ES]]''' ||  || [[Evolution Strategies]]
|-
| '''[[ESN]]''' ||  || [[Echo State Network]]
|-
| '''[[ETL]]''' ||  || [[Extract, Transform, Load]]
|-
| '''[[ETL Pipeline]]''' ||  || [[Extract Transform Load Pipeline]]
|-
| '''[[EXT]]''' ||  || [[Extremely Randomized Trees]]
|-
| '''[[F1]]''' ||  || [[F1 Score (harmonic mean of precision and recall)]]
|-
| '''[[F1 Score]]''' ||  || [[Harmonic Precision-Recall Mean]]
|-
| '''[[FALA]]''' ||  || [[Finite Action-set Learning Automata]]
|-
| '''[[Fast R-CNN]]''' ||  || [[Faster Region-based Convolutional Neural Network]]
|-
| '''[[FC]]''' ||  || [[Fully-Connected]]
|-
| '''[[FC-CNN]]''' ||  || [[Fully Convolutional Convolutional Neural Network]]
|-
| '''[[FC-LSTM]]''' ||  || [[Fully Connected Long Short-Term Memory]]
|-
| '''[[FCM]]''' ||  || [[Fuzzy C-Means]]
|-
| '''[[FCN]]''' ||  || [[Fully Convolutional Network]]
|-
| '''[[FER]]''' ||  || [[Facial Expression Recognition]]
|-
| '''[[FFT]]''' ||  || [[Fast Fourier transform]]
|-
| '''[[FL]]''' ||  || [[Federated Learning]]
|-
| '''[[FLOP]]''' ||  || [[Floating Point Operations]]
|-
| '''[[FLOPS]]''' ||  || [[Floating Point Operations Per Second]]
|-
| '''[[FM]]''' ||  || [[Foundation model]]
|-
| '''[[FN]]''' ||   || [[False negative]]
|-
| '''[[FNN]]''' ||  || [[Feedforward Neural Network]]
|-
| '''[[FNR]]''' ||  || [[False negative rate]]
|-
| '''[[FOAF]]''' ||  || [[Friend of a Friend (ontology)]]
|-
| '''[[FP]]''' ||  || [[False positive]]
|-
| '''[[FPGA]]''' ||  || [[Field-Programmable Gate Array]]
|-
| '''[[FPN]]''' ||  || [[Feature Pyramid Network]]
|-
| '''[[FPR]]''' ||  || [[False positive rate]]
|-
| '''[[FST]]''' ||  || [[Finite state transducer]]
|-
| '''[[FTL]]''' ||  || [[Few-Shot Learning]]
|-
| '''[[FWA]]''' ||  || [[Fireworks Algorithm]]
|-
| '''[[FWIoU]]''' ||  || [[Frequency Weighted Intersection over Union]]
|-
|-
| '''[[FNR]]''' || || [[False negative rate]]
| '''[[GA]]''' ||   || [[Genetic Algorithm]]
|-
|-
| '''[[FP]]''' || || [[False positive]]
| '''[[GALE]]''' ||   || [[Global Aggregations of Local Explanations]]
|-
|-
| '''[[FPR]]''' || || [[False positive rate]]
| '''[[GAM]]''' ||   || [[Generalized Additive Model]]
|-
|-
| '''[[GAN]]''' ||  || [[Generative Adversarial Network]]
| '''[[GAN]]''' ||  || [[Generative Adversarial Network]]
|-
| '''[[GAP]]''' ||  || [[Global Average Pooling]]
|-
| '''[[GBDT]]''' ||  || [[Gradient Boosted Decision Tree]]
|-
|-
| '''[[GBM]]''' ||  || [[Gradient Boosting Machine]]
| '''[[GBM]]''' ||  || [[Gradient Boosting Machine]]
|-
|-
| '''[[GD]]''' || || [[Gradient descent]]
| '''[[GBRCN]]''' ||  || [[Gradient-Boosting Random Convolutional Network]]
|-
| '''[[GD]]''' ||   || [[Gradient descent]]
|-
| '''[[GEBI]]''' ||  || [[Global Explanation for Bias Identification]]
|-
| '''[[GFNN]]''' ||  || [[Gradient frequency neural network]]
|-
|-
| '''[[GFNN]]''' || || [[Gradient frequency neural network]]
| '''[[GLCM]]''' ||   || [[Gray Level Co-occurrence Matrix]]
|-
|-
| '''[[GLM]]''' ||  || [[Generalized Linear Model]]
| '''[[GLM]]''' ||  || [[Generalized Linear Model]]
|-
|-
| '''[[GloVE]]''' || || [[Global Vectors]]
| '''[[GLOM]]''' ||  || [[A neural network architecture by Geoffrey Hinton]]
|-
| '''[[Gloss2Text]]''' ||  || [[A task of transforming raw glosses into meaningful sentences.]]
|-
| '''[[GloVE]]''' ||   || [[Global Vectors]]
|-
| '''[[GLPK]]''' ||  || [[GNU Linear Programming Kit]]
|-
|-
| '''[[GLUE]]''' ||  || [[General Language Understanding Evaluation]]
| '''[[GLUE]]''' ||  || [[General Language Understanding Evaluation]]
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| '''[[GMM]]''' ||  || [[Gaussian mixture model]]
| '''[[GMM]]''' ||  || [[Gaussian mixture model]]
|-
|-
| '''[[GPR]]''' || || [[Gaussian process regression]]
| '''[[GP]]''' ||  || [[Genetic Programming]]
|-
| '''[[GPR]]''' ||   || [[Gaussian process regression]]
|-
|-
| '''[[GPT]]''' ||  || [[Generative Pre-Training]]
| '''[[GPT]]''' ||  || [[Generative Pre-Training]]
|-
|-
| '''[[GPU]]''' ||  || [[Graphics processing unit]]
| '''[[GPU]]''' ||  || [[Graphics processing unit]]
|-
| '''[[GradCAM]]''' ||  || [[GRADient-weighted Class Activation Mapping]]
|-
|-
| '''[[GRU]]''' ||  || [[Gated recurrent unit]]
| '''[[GRU]]''' ||  || [[Gated recurrent unit]]
|-
|-
| '''[[HAN]]''' || || [[Hierarchical Attention Networks]]
| '''[[Gurobi]]''' ||  || [[An optimization solver (named after its founders, Zonghao Gu, Edward Rothberg, and Robert Bixby)]]
|-
| '''[[HamNoSys]]''' ||  || [[Hamburg Sign Language Notation System]]
|-
| '''[[HAN]]''' ||   || [[Hierarchical Attention Networks]]
|-
| '''[[HC]]''' ||  || [[Hierarchical Clustering]]
|-
| '''[[HCA]]''' ||  || [[Hierarchical Clustering Analysis]]
|-
| '''[[HDP]]''' ||  || [[Hierarchical Dirichlet process]]
|-
| '''[[HF]]''' ||  || [[Hugging Face]]
|-
| '''[[HHDS]]''' ||  || [[HipHop Dataset]]
|-
| '''[[hLDA]]''' ||  || [[Hierarchical Latent Dirichlet allocation]]
|-
| '''[[HMM]]''' ||  || [[Hidden Markov Model]]
|-
| '''[[HNN]]''' ||  || [[Hopfield Neural Network]]
|-
| '''[[HOG]]''' ||  || [[Histogram of Oriented Gradients (feature descriptor)]]
|-
| '''[[Hopfield]]''' ||  || [[Hopfield Network]]
|-
| '''[[HPC]]''' ||  || [[High Performance Computing]]
|-
| '''[[HRED]]''' ||  || [[Hierarchical Recurrent Encoder-Decoder]]
|-
| '''[[HRI]]''' ||  || [[Human-Robot Interaction]]
|-
| '''[[HSMM]]''' ||  || [[Hidden Semi-Markov Model]]
|-
| '''[[HTM]]''' ||  || [[Hierarchical Temporal Memory]]
|-
|-
| '''[[HF]]''' || || [[Hugging Face]]
| '''[[i.i.d]]''' ||   || [[Independent and Identically Distributed]]
|-
|-
| '''[[i.i.d.]]''' ||  || [[Independently and identically distributed]]
| '''[[i.i.d.]]''' ||  || [[Independently and identically distributed]]
|-
| '''[[IaaS]]''' ||  || [[Infrastructure as a Service]]
|-
|-
| '''[[ICA]]''' ||  || [[Independent component analysis]]
| '''[[ICA]]''' ||  || [[Independent component analysis]]
|-
| '''[[ICP]]''' ||  || [[Iterative Closest Point (point cloud registration)]]
|-
| '''[[ID3]]''' ||  || [[Iterative Dichotomiser 3]]
|-
| '''[[IDA*]]''' ||  || [[Iterative Deepening A* Search Algorithm]]
|-
| '''[[IDR]]''' ||  || [[Input dependence rate]]
|-
| '''[[IG]]''' ||  || [[Invariant Generation]]
|-
|-
| '''[[IID]]''' ||  || [[Independently and identically distributed]]
| '''[[IID]]''' ||  || [[Independently and identically distributed]]
|-
| '''[[IIR]]''' ||  || [[Input independence rate]]
|-
| '''[[ILASP]]''' ||  || [[Inductive Learning of Answer Set Programs]]
|-
| '''[[ILP]]''' ||  || [[Integer Linear Programming]]
|-
| '''[[INFD]]''' ||  || [[Explanation Infidelity]]
|-
| '''[[IoA]]''' ||  || [[Internet of Agents]]
|-
| '''[[IoE]]''' ||  || [[Internet of Everything]]
|-
| '''[[IoT]]''' ||  || [[Internet of Things]]
|-
| '''[[IoU]]''' ||  || [[Jaccard index (intersection over union)]]
|-
| '''[[IR]]''' ||  || [[Information Retrieval]]
|-
| '''[[IRCoT]]''' ||  || [[Interleaving Retrieval CoT]]
|-
| '''[[ISIC]]''' ||  || [[International Skin Imaging Collaboration]]
|-
| '''[[IVR]]''' ||  || [[Interactive Voice Response]]
|-
| '''[[K-Means]]''' ||  || [[K-Means Clustering]]
|-
| '''[[KB]]''' ||  || [[Knowledge Base]]
|-
| '''[[KDE]]''' ||  || [[Kernel Density Estimation]]
|-
| '''[[KF]]''' ||  || [[Kalman Filter]]
|-
|-
| '''[[kFCV]]''' ||  || [[K-fold cross validation]]
| '''[[kFCV]]''' ||  || [[K-fold cross validation]]
|-
| '''[[KL]]''' ||  || [[Kullback Leibler (KL) divergence]]
|-
|-
| '''[[KNN]]''' ||  || [[K-nearest neighbors]]
| '''[[KNN]]''' ||  || [[K-nearest neighbors]]
|-
| '''[[KR]]''' ||  || [[Knowledge Representation]]
|-
| '''[[KRR]]''' ||  || [[Kernel Ridge Regression]]
|-
|-
| '''[[LAION]]''' ||  || [[Large-scale Artificial Intelligence Open Network]]
| '''[[LAION]]''' ||  || [[Large-scale Artificial Intelligence Open Network]]
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|-
|-
| '''[[LaMDA]]''' ||  || [[Language Models for Dialog Applications]]
| '''[[LaMDA]]''' ||  || [[Language Models for Dialog Applications]]
|-
| '''[[LBP]]''' ||  || [[Local Binary Pattern (texture descriptor)]]
|-
| '''[[LDA]]''' ||  || [[Latent Dirichlet Allocation]]
|-
| '''[[LDADE]]''' ||  || [[Latent Dirichlet Allocation Differential Evolution]]
|-
| '''[[LEPOR]]''' ||  || [[Language Evaluation Portal]]
|-
| '''[[LightGBM]]''' ||  || [[Light Gradient Boosting Machine]]
|-
| '''[[LIME]]''' ||  || [[Local Interpretable Model-agnostic Explanations]]
|-
| '''[[LINGO]]''' ||  || [[A software for linear, nonlinear, and integer optimization]]
|-
| '''[[LL]]''' ||  || [[Lifelong learning]]
|-
|-
| '''[[LLM]]''' ||  || [[Large language model]]
| '''[[LLM]]''' ||  || [[Large language model]]
|-
|-
| '''[[LLS]]''' ||  || [[Linear least squares]]
| '''[[LLS]]''' ||  || [[Linear least squares]]
|-
| '''[[LMNN]]''' ||  || [[Large Margin Nearest Neighbor]]
|-
|-
| '''[[LoLM]]''' ||  || [[Lots of Little Models]]
| '''[[LoLM]]''' ||  || [[Lots of Little Models]]
|-
| '''[[LP]]''' ||  || [[Linear Programming]]
|-
|-
| '''[[LPAQA]]''' ||  || [[Language model Prompt And Query Archive]]
| '''[[LPAQA]]''' ||  || [[Language model Prompt And Query Archive]]
|-
| '''[[LRP]]''' ||  || [[Layer-wise Relevance Propagation]]
|-
| '''[[LSA]]''' ||  || [[Latent semantic analysis]]
|-
| '''[[LSI]]''' ||  || [[Latent Semantic Indexing]]
|-
|-
| '''[[LSTM]]''' ||  || [[Long short-term memory]]
| '''[[LSTM]]''' ||  || [[Long short-term memory]]
|-
| '''[[LSTM-CRF]]''' ||  || [[Long Short-Term Memory with Conditional Random Field]]
|-
| '''[[LTR]]''' ||  || [[Learning To Rank]]
|-
| '''[[LVQ]]''' ||  || [[Learning Vector Quantization]]
|-
| '''[[M2M]]''' ||  || [[Machine to Machine]]
|-
| '''[[MADE]]''' ||  || [[Masked Autoencoder for Distribution Estimation]]
|-
|-
| '''[[MAE]]''' ||  || [[Mean absolute error]]
| '''[[MAE]]''' ||  || [[Mean absolute error]]
|-
| '''[[MAF]]''' ||  || [[Masked Autoregressive Flows]]
|-
| '''[[MAIRL]]''' ||  || [[Multi-Agent Inverse Reinforcement Learning]]
|-
| '''[[MAP]]''' ||  || [[Maximum A Posteriori (MAP) Estimation]]
|-
|-
| '''[[MAPE]]''' ||  || [[Mean absolute percentage error]]
| '''[[MAPE]]''' ||  || [[Mean absolute percentage error]]
|-
| '''[[MARL]]''' ||  || [[Multi-Agent Reinforcement Learning]]
|-
| '''[[MART]]''' ||  || [[Multiple Additive Regression Tree]]
|-
| '''[[MaxEnt]]''' ||  || [[Maximum Entropy]]
|-
| '''[[MAXSAT]]''' ||  || [[Maximum Satisfiability Problem]]
|-
| '''[[MCLNN]]''' ||  || [[Masked ConditionaL Neural Networks]]
|-
| '''[[MCMC]]''' ||  || [[Markov Chain Monte Carlo]]
|-
| '''[[MCS]]''' ||  || [[Model contrast score]]
|-
| '''[[MCTS]]''' ||  || [[Monte Carlo Tree Search]]
|-
| '''[[MDL]]''' ||  || [[Minimum description length (MDL) principle]]
|-
| '''[[MDN]]''' ||  || [[Mixture Density Network]]
|-
| '''[[MDP]]''' ||  || [[Markov Decision Process]]
|-
| '''[[MDRNN]]''' ||  || [[Multidimensional recurrent neural network]]
|-
| '''[[MER]]''' ||  || [[Music Emotion Recognition]]
|-
| '''[[METEOR]]''' ||  || [[Metric for Evaluation of Translation with Explicit ORdering]]
|-
| '''[[MIL]]''' ||  || [[Multiple Instance Learning]]
|-
| '''[[MILP]]''' ||  || [[Mixed-Integer Linear Programming]]
|-
| '''[[MINT]]''' ||  || [[Mutual Information based Transductive Feature Selection]]
|-
| '''[[MIoU]]''' ||  || [[Mean Intersection over Union]]
|-
| '''[[MIP]]''' ||  || [[Mixed-Integer Programming]]
|-
| '''[[ML]]''' ||  || [[Machine learning]]
|-
| '''[[MLaaS]]''' ||  || [[Machine Learning as a Service]]
|-
| '''[[MLE]]''' ||  || [[Maximum Likelihood Estimation]]
|-
| '''[[MLLM]]''' ||  || [[Multimodal large language model]]
|-
| '''[[MLM]]''' ||  || [[Music Language Models]]
|-
| '''[[MLP]]''' ||  || [[Multi-Layer Perceptron]]
|-
| '''[[MMI]]''' ||  || [[Maximum Mutual Information]]
|-
|-
| '''[[MNIST]]''' ||  || [[Modified National Institute of Standards and Technology]]
| '''[[MNIST]]''' ||  || [[Modified National Institute of Standards and Technology]]
|-
| '''[[MOEA]]''' ||  || [[Multi-Objective Evolutionary Algorithm]]
|-
| '''[[MPA]]''' ||  || [[Mean Pixel Accuracy]]
|-
| '''[[MR]]''' ||  || [[Mixed Reality]]
|-
| '''[[MRF]]''' ||  || [[Markov Random Field]]
|-
| '''[[MRR]]''' ||  || [[Mean Reciprocal Rank]]
|-
| '''[[MRS]]''' ||  || [[Music Recommender System]]
|-
| '''[[MSDAE]]''' ||  || [[Modified Sparse Denoising Autoencoder]]
|-
|-
| '''[[MSE]]''' ||  || [[Mean squared error]]
| '''[[MSE]]''' ||  || [[Mean squared error]]
|-
|-
| '''[[ML]]''' ||  || [[Machine learning]]
| '''[[MSR]]''' ||  || [[Music Style Recognition]]
|-
| '''[[MTL]]''' ||  || [[Multi-Task Learning]]
|-
| '''[[NARX]]''' ||  || [[Nonlinear AutoRegressive with eXogenous input (neural network model)]]
|-
| '''[[NAS]]''' ||  || [[Neural Architecture Search]]
|-
| '''[[NB]]''' ||  || [[Na ̈ıve Bayes]]
|-
| '''[[NBKE]]''' ||  || [[Na ̈ıve Bayes with Kernel Estimation]]
|-
| '''[[NDCG]]''' ||  || [[Normalized Discounted Cumulative Gain]]
|-
| '''[[NE]]''' ||  || [[Nash Equilibrium (in game theory)]]
|-
|-
| '''[[MLLM]]''' ||  || [[Multimodal large language model]]
| '''[[NEAT]]''' ||  || [[NeuroEvolution of Augmenting Topologies]]
|-
|-
| '''[[NER]]''' ||  || [[Named entity recognition]]
| '''[[NER]]''' ||  || [[Named entity recognition]]
|-
|-
| '''[[NST]]''' ||  || [[Neural style transfer]]
| '''[[NERQ]]''' ||  || [[Named Entity Recognition in Query]]
|-
| '''[[NEST]]''' ||  || [[Neural Simulation Tool]]
|-
| '''[[NF]]''' ||  || [[Normalizing Flow]]
|-
| '''[[NFL]]''' ||  || [[No Free Lunch (NFL) theorem]]
|-
| '''[[NISQ]]''' ||  || [[Noisy Intermediate-Scale Quantum (quantum computing)]]
|-
| '''[[NLG]]''' ||  || [[Natural Language Generation]]
|-
|-
| '''[[NLP]]''' ||  || [[Natural Language Processing]]
| '''[[NLP]]''' ||  || [[Natural Language Processing]]
|-
| '''[[NLT]]''' ||  || [[Neural Machine Translation]]
|-
|-
| '''[[NLU]]''' ||  || [[Natural Language Understanding]]
| '''[[NLU]]''' ||  || [[Natural Language Understanding]]
|-
|-
| '''[[NMF]]''' ||  || [[Non-negative matrix factorization]]
| '''[[NMF]]''' ||  || [[Non-negative matrix factorization]]
|-
| '''[[NMS]]''' ||  || [[Non Maximum Suppression]]
|-
| '''[[NMT]]''' ||  || [[Neural Machine Translation]]
|-
|-
| '''[[NN]]''' ||  || [[Neural network]]
| '''[[NN]]''' ||  || [[Neural network]]
|-
| '''[[NNMODFF]]''' ||  || [[Neural Network based Multi-Onset Detection Function Fusion]]
|-
| '''[[NPE]]''' ||  || [[Neural Physical Engine]]
|-
| '''[[NRMSE]]''' ||  || [[Normalized RMSE]]
|-
| '''[[NSGA-II]]''' ||  || [[Non-dominated Sorting Genetic Algorithm II]]
|-
| '''[[NST]]''' ||  || [[Neural style transfer]]
|-
| '''[[NTM]]''' ||  || [[Neural Turing Machine]]
|-
| '''[[NuSVC]]''' ||  || [[Nu-Support Vector Classification]]
|-
| '''[[NuSVR]]''' ||  || [[Nu-Support Vector Regression]]
|-
|-
| '''[[OBM]]''' ||  || [[One Big Model]]
| '''[[OBM]]''' ||  || [[One Big Model]]
|-
|-
| '''[[OCR]]''' ||  || [[Optical character recognition]]
| '''[[OCR]]''' ||  || [[Optical character recognition]]
|-
| '''[[OD]]''' ||  || [[Object Detection]]
|-
| '''[[ODF]]''' ||  || [[Onset Detection Function]]
|-
| '''[[OIL]]''' ||  || [[Ontology Inference Layer]]
|-
| '''[[OLR]]''' ||  || [[Ordinary Linear Regression]]
|-
| '''[[OLS]]''' ||  || [[Ordinary Least Squares]]
|-
| '''[[OMNeT++]]''' ||  || [[Objective Modular Network Testbed in C++]]
|-
| '''[[OMR]]''' ||  || [[Optical Mark Recognition]]
|-
|-
| '''[[OOF]]''' ||  || [[Out-of-fold]]
| '''[[OOF]]''' ||  || [[Out-of-fold]]
|-
| '''[[ORB]]''' ||  || [[Oriented FAST and Rotated BRIEF (feature descriptor)]]
|-
| '''[[OWL]]''' ||  || [[Web Ontology Language]]
|-
| '''[[PA]]''' ||  || [[Pixel Accuracy]]
|-
| '''[[PaaS]]''' ||  || [[Platform as a Service]]
|-
| '''[[PACO]]''' ||  || [[Poisson Additive Co-Clustering]]
|-
|-
| '''[[PaLM]]''' ||  || [[Pathways Language Model]]
| '''[[PaLM]]''' ||  || [[Pathways Language Model]]
|-
| '''[[PBAC]]''' ||  || [[Policy-Based Access Control]]
|-
|-
| '''[[PCA]]''' ||  || [[Principal component analysis]]
| '''[[PCA]]''' ||  || [[Principal component analysis]]
|-
| '''[[PCL]]''' ||  || [[Point Cloud Library (3D perception)]]
|-
| '''[[PECS]]''' ||  || [[Physics Engine for Collaborative Simulation]]
|-
| '''[[PEGASUS]]''' ||  || [[Pre-training with Extracted Gap-Sentences for Abstractive Summarization]]
|-
| '''[[PF]]''' ||  || [[Particle Filter]]
|-
|-
| '''[[PFE]]''' ||  || [[Probabilistic facial embedding]]
| '''[[PFE]]''' ||  || [[Probabilistic facial embedding]]
|-
| '''[[PLSI]]''' ||  || [[Probabilistic Latent Semantic Indexing]]
|-
| '''[[PM]]''' ||  || [[Project Manager]]
|-
| '''[[PMF]]''' ||  || [[Probabilistic Matrix Factorization]]
|-
| '''[[PMI]]''' ||  || [[Pointwise Mutual Information]]
|-
| '''[[PNN]]''' ||  || [[Probabilistic Neural Network]]
|-
| '''[[POC]]''' ||  || [[Proof of Concept]]
|-
| '''[[POMDP]]''' ||  || [[Partially Observable Markov Decision Process]]
|-
| '''[[POS]]''' ||  || [[Part of Speech (POS) Tagging]]
|-
| '''[[POT]]''' ||  || [[Partially Observable Tree (decision-making under uncertainty)]]
|-
| '''[[PPL]]''' ||  || [[Perplexity (a measure of language model performance)]]
|-
| '''[[PPMI]]''' ||  || [[Positive Pointwise Mutual Information]]
|-
|-
| '''[[PPO]]''' ||  || [[Proximal Policy Optimization]]
| '''[[PPO]]''' ||  || [[Proximal Policy Optimization]]
|-
| '''[[PReLU]]''' ||  || [[Parametric Rectified Linear Unit-Yor Topic Modeling]]
|-
| '''[[PRM]]''' ||  || [[Probabilistic Roadmap (motion planning algorithm)]]
|-
| '''[[PSO]]''' ||  || [[Particle Swarm Optimization]]
|-
| '''[[PU]]''' ||  || [[Positive Unlabaled]]
|-
| '''[[PYTM]]''' ||  || [[Pitman]]
|-
| '''[[QA]]''' ||  || [[Question Answering]]
|-
| '''[[QAOA]]''' ||  || [[Quantum Approximate Optimization Algorithm]]
|-
| '''[[QAP]]''' ||  || [[Quadratic Assignment Problem]]
|-
| '''[[QEC]]''' ||  || [[Quantum Error Correction]]
|-
| '''[[QFT]]''' ||  || [[Quantum Fourier Transform]]
|-
| '''[[QIP]]''' ||  || [[Quantum Information Processing]]
|-
| '''[[QKD]]''' ||  || [[Quantum Key Distribution]]
|-
| '''[[QML]]''' ||  || [[Quantum Machine Learning]]
|-
| '''[[QNN]]''' ||  || [[Quantum Neural Network]]
|-
| '''[[QP]]''' ||  || [[Quadratic Programming]]
|-
| '''[[QPE]]''' ||  || [[Quantum Phase Estimation]]
|-
| '''[[R-CNN]]''' ||  || [[Region-based Convolutional Neural Network]]
|-
|-
| '''[[R2]]''' ||  || [[R-squared]]
| '''[[R2]]''' ||  || [[R-squared]]
|-
|-
| '''[[RF]]''' ||  || [[Random forest]]
| '''[[RandNN]]''' ||  || [[Random Neural Network]]
|-
| '''[[RANSAC]]''' ||  || [[RANdom SAmple Consensus]]
|-
| '''[[RBAC]]''' ||  || [[Rule-Based Access Control]]
|-
| '''[[RBF]]''' ||  || [[Radial Basis Function]]
|-
| '''[[RBFNN]]''' ||  || [[Radial Basis Function Neural Network]]
|-
| '''[[RBM]]''' ||  || [[Restricted Boltzmann Machine]]
|-
| '''[[RDF]]''' ||  || [[Resource Description Framework]]
|-
| '''[[ReAct]]''' ||  || [[Reason + Act]]
|-
|-
| '''[[REALM]]''' ||  || [[Retrieval-Augmented Language Model Pre-Training]]
| '''[[REALM]]''' ||  || [[Retrieval-Augmented Language Model Pre-Training]]
|-
| '''[[ReCAPTCHA]]''' ||  || [[Reverse CAPTCHA]]
|-
|-
| '''[[ReLU]]''' ||  || [[Rectified Linear Unit]]
| '''[[ReLU]]''' ||  || [[Rectified Linear Unit]]
|-
| '''[[REPTree]]''' ||  || [[Reduced Error Pruning Tree]]
|-
|-
| '''[[RETRO]]''' ||  || [[Retrieval Enhanced Transformer]]
| '''[[RETRO]]''' ||  || [[Retrieval Enhanced Transformer]]
|-
| '''[[RF]]''' ||  || [[Random forest]]
|-
|-
| '''[[RFE]]''' ||  || [[Recursive Feature Elimination]]
| '''[[RFE]]''' ||  || [[Recursive Feature Elimination]]
|-
| '''[[RGB]]''' ||  || [[Red Green Blue color model]]
|-
| '''[[RICNN]]''' ||  || [[Rotation Invariant Convolutional Neural Network]]
|-
| '''[[RIM]]''' ||  || [[Recurrent Interence Machines]]
|-
| '''[[RIPPER]]''' ||  || [[Repeated Incremental Pruning to Produce Error Reduction]]
|-
| '''[[RISE]]''' ||  || [[Random Interval Spectral Ensemble]]
|-
|-
| '''[[RL]]''' ||  || [[Reinforcement learning]]
| '''[[RL]]''' ||  || [[Reinforcement learning]]
|-
| '''[[RLFM]]''' ||  || [[Regression based latent factors]]
|-
|-
| '''[[RLHF]]''' ||  || [[Reinforcement Learning from Human Feedback]]
| '''[[RLHF]]''' ||  || [[Reinforcement Learning from Human Feedback]]
|-
|-
| '''[[RMSLE]]''' ||  || [[Root mean squared logarithmic error ]]
| '''[[RMSE]]''' ||  || [[Root mean squared error]]
|-
|-
| '''[[RMSE]]''' ||  || [[Root mean squared error ]]
| '''[[RMSLE]]''' ||  || [[Root mean squared logarithmic error]]
|-
| '''[[RMSprop]]''' ||  || [[Root Mean Square Propagation]]
|-
|-
| '''[[RNN]]''' ||  || [[Recurrent neural network]]
| '''[[RNN]]''' ||  || [[Recurrent neural network]]
|-
|-
| '''[[RoBERTa]]''' || || [[Robustly Optimized BERT Pretraining Approach]]
| '''[[RNNLM]]''' ||  || [[Recurrent Neural Network Language Model (RNNLM)]]
|-
| '''[[RoBERTa]]''' ||   || [[Robustly Optimized BERT Pretraining Approach]]
|-
|-
| '''[[ROC]]''' ||  || [[Receiver operating characteristic]]
| '''[[ROC]]''' ||  || [[Receiver operating characteristic]]
|-
| '''[[ROI]]''' ||  || [[Region Of Interest]]
|-
| '''[[ROS]]''' ||  || [[Robot Operating System]]
|-
| '''[[ROUGE]]''' ||  || [[Recall-Oriented Understudy for Gisting Evaluation (NLP metric)]]
|-
|-
| '''[[RPA]]''' ||  || [[Robotic Process Automation]]
| '''[[RPA]]''' ||  || [[Robotic Process Automation]]
|-
| '''[[RR]]''' ||  || [[Ridge Regression]]
|-
| '''[[RRT]]''' ||  || [[Rapidly-exploring Random Tree (motion planning algorithm)]]
|-
|-
| '''[[RSI]]''' ||  || [[Recursive self-improvement]]
| '''[[RSI]]''' ||  || [[Recursive self-improvement]]
|-
| '''[[RTRL]]''' ||  || [[Real-Time Recurrent Learning]]
|-
| '''[[SA]]''' ||  || [[Simulated Annealing]], [[Segment Anything]]
|-
| '''[[SAM]]''' ||  || [[Segment Anything Model]]
|-
| '''[[SaaS]]''' ||  || [[Software as a Service]]
|-
| '''[[SAC]]''' ||  || [[Soft Actor-Critic]]
|-
| '''[[SAE]]''' ||  || [[Stacked AE]]
|-
| '''[[SARSA]]''' ||  || [[State-Action-Reward-State-Action]]
|-
| '''[[SAT]]''' ||  || [[Satisfiability Problem]]
|-
| '''[[SBAC]]''' ||  || [[Situation-Based Access Control]]
|-
| '''[[SBM]]''' ||  || [[Stochastic block model]]
|-
| '''[[SBO]]''' ||  || [[Structured Bayesian optimization]]
|-
| '''[[SBSE]]''' ||  || [[Search-based software engineering]]
|-
| '''[[SCH]]''' ||  || [[Stochastic convex hull]]
|-
| '''[[SCIP]]''' ||  || [[Solving Constraint Integer Programs]]
|-
| '''[[SDAE]]''' ||  || [[Stacked DAE]]
|-
| '''[[seq2seq]]''' ||  || [[Sequence to Sequence Learning]]
|-
| '''[[SER]]''' ||  || [[Sentence Error Rate]]
|-
| '''[[SGBoost]]''' ||  || [[Stochastic Gradient Boosting]]
|-
|-
| '''[[SGD]]''' ||  || [[Stochastic gradient descent]]
| '''[[SGD]]''' ||  || [[Stochastic gradient descent]]
|-
| '''[[SGVB]]''' ||  || [[Stochastic Gradient Variational Bayes]]
|-
| '''[[SHAP]]''' ||  || [[SHapley Additive exPlanation]]
|-
| '''[[SHLLE]]''' ||  || [[Supervised Hessian Locally Linear Embedding]]
|-
| '''[[SIFT]]''' ||  || [[Scale-Invariant Feature Transform (feature detection)]]
|-
| '''[[Sign2(Gloss+Text)]]''' ||  || [[Sign to Gloss and Text]]
|-
| '''[[Sign2Gloss]]''' ||  || [[A one to one translation from the single sign to the single gloss.]]
|-
| '''[[Sign2Text]]''' ||  || [[A task of full translation from the sign language into the spoken one]]
|-
|-
| '''[[SL]]''' ||  || [[Supervised learning]]
| '''[[SL]]''' ||  || [[Supervised learning]]
|-
| '''[[SLAM]]''' ||  || [[Simultaneous Localization and Mapping]]
|-
| '''[[SLDS]]''' ||  || [[Switching Linear Dynamical System]]
|-
| '''[[SLP]]''' ||  || [[Single-Layer Perceptron]]
|-
| '''[[SLRT]]''' ||  || [[Sign Language Recognition Transformer]]
|-
| '''[[SLT]]''' ||  || [[Sign Language Translation]]
|-
| '''[[SLTT]]''' ||  || [[Sign Language Translation Transformer]]
|-
| '''[[SMA*]]''' ||  || [[Simplified Memory-bounded A* Search Algorithm]]
|-
| '''[[SMBO]]''' ||  || [[Sequential Model-Based Optimization]]
|-
| '''[[SMO]]''' ||  || [[Sequential Minimal Optimization]]
|-
| '''[[SMOTE]]''' ||  || [[Synthetic Minority Over-sampling Technique]]
|-
| '''[[SNN]]''' ||  || [[Sparse Neural Network]]
|-
| '''[[SOM]]''' ||  || [[Self-Organizing Map]]
|-
| '''[[SOTA]]''' ||  || [[State of the Art]]
|-
| '''[[SPARQL]]''' ||  || [[SPARQL Protocol and RDF Query Language]]
|-
| '''[[Spiking NN]]''' ||  || [[Spiking Neural Network]]
|-
| '''[[SPM]]''' ||  || [[SentencePiece Model (subword tokenization)]]
|-
| '''[[SpRay]]''' ||  || [[Spectral Relevance Analysis]]
|-
| '''[[SSD]]''' ||  || [[Single-Shot Detector]]
|-
| '''[[SSL]]''' ||  || [[Self-Supervised Learning]]
|-
| '''[[SSVM]]''' ||  || [[Smooth support vector machine]]
|-
|-
| '''[[ST]]''' ||  || [[Style transfer]]
| '''[[ST]]''' ||  || [[Style transfer]]
|-
|-
| '''[[STaR]]''' ||  || [[Self-Taught Reasoner]]
| '''[[STaR]]''' ||  || [[Self-Taught Reasoner]]
|-
| '''[[STDA]]''' ||  || [[Style Transfer Data Augmentation]]
|-
| '''[[STDP]]''' ||  || [[Spike Timing-Dependent Plasticity]]
|-
| '''[[STL]]''' ||  || [[Selt-Taught Learning]]
|-
| '''[[SUMO]]''' ||  || [[Simulation of Urban Mobility]]
|-
| '''[[SURF]]''' ||  || [[Speeded-Up Robust Features (feature detection)]]
|-
| '''[[SVC]]''' ||  || [[Support Vector Classification]]
|-
| '''[[SVD]]''' ||  || [[Singing Voice Detection]]
|-
|-
| '''[[SVM]]''' ||  || [[Support vector machine]]
| '''[[SVM]]''' ||  || [[Support vector machine]]
|-
|-
| '''[[SVR]]''' ||  || [[Support Vector Regression]]
| '''[[SVR]]''' ||  || [[Support Vector Regression]]
|-
| '''[[SVS]]''' ||  || [[Singing Voice Separation]]
|-
| '''[[SWI-Prolog]]''' ||  || [[Semantic Web Interface for Prolog]]
|-
| '''[[t-SNE]]''' ||  || [[t-distributed stochastic neighbor embedding]]
|-
|-
| '''[[T5]]''' ||  || [[Text-To-Text Transfer Transformer]]
| '''[[T5]]''' ||  || [[Text-To-Text Transfer Transformer]]
|-
| '''[[TD]]''' ||  || [[Temporal Difference]]
|-
| '''[[TDA]]''' ||  || [[Targeted Data Augmentation]]
|-
| '''[[TDE]]''' ||  || [[Time Domain Ensemble]]
|-
|-
| '''[[tf-idf]]''' ||  || [[term frequency–inverse document frequency]]
| '''[[tf-idf]]''' ||  || [[term frequency–inverse document frequency]]
|-
|-
| '''[[TN]]''' || || [[True negative]]
| '''[[TGAN]]''' ||  || [[Temporal Generative Adversarial Network]]
|-
| '''[[THAID]]''' ||  || [[THeta Automatic Interaction Detection]]
|-
| '''[[TINT]]''' ||  || [[Tree-Interpreter]]
|-
| '''[[TL]]''' ||  || [[Transfer Learning]]
|-
| '''[[TLFN]]''' ||  || [[Time-Lagged Feedforward Neural Network]]
|-
| '''[[TN]]''' ||   || [[True negative]]
|-
| '''[[TNR]]''' ||  || [[True negative rate]]
|-
| '''[[ToM]]''' ||  || [[Theory of Mind]]
|-
| '''[[TP]]''' ||  || [[True positive]]
|-
| '''[[TPOT]]''' ||  || [[Tree-based Pipeline Optimization Tool]]
|-
| '''[[TPR]]''' ||  || [[True positive rate]]
|-
| '''[[TPU]]''' ||  || [[Tensor Processing Unit]]
|-
| '''[[TRPO]]''' ||  || [[Trust Region Policy Optimization]]
|-
| '''[[TS]]''' ||  || [[Tabu Search]]
|-
| '''[[TSF]]''' ||  || [[Time Series Forest]]
|-
|-
| '''[[TNR]]''' || || [[True negative rate]]
| '''[[TSP]]''' ||   || [[Traveling Salesman Problem]]
|-
|-
| '''[[ToM]]''' || || [[Theory of Mind]]
| '''[[TTS]]''' ||   || [[Text-to-Speech]]
|-
|-
| '''[[TP]]''' || || [[True positive]]
| '''[[UCT]]''' ||   || [[Upper Confidence bounds applied to Trees (Monte Carlo Tree Search variant)]]
|-
|-
| '''[[TPR]]''' || || [[True positive rate]]
| '''[[UDA]]''' ||   || [[Unsupervised Data Augmentation]]
|-
|-
| '''[[t-SNE]]''' ||  || [[t-distributed stochastic neighbor embedding]]
| '''[[UKF]]''' ||  || [[Unscented Kalman Filter]]
|-
|-
| '''[[UL]]''' ||  || [[Unsupervised learning]]
| '''[[UL]]''' ||  || [[Unsupervised learning]]
|-
| '''[[ULMFiT]]''' ||  || [[Universal Language Model Fine-Tuning]]
|-
| '''[[UMAP]]''' ||  || [[Uniform Manifold Approximation and Projection]]
|-
| '''[[USM]]''' ||  || [[Universal Speech Model]]
|-
| '''[[V-Net]]''' ||  || [[Volumetric Convolutional neural network]]
|-
| '''[[VAD]]''' ||  || [[Voice Activity Detection]]
|-
| '''[[VAE]]''' ||  || [[Variational AutoEncoder]]
|-
| '''[[VGG]]''' ||  || [[Visual Geometry Group]]
|-
| '''[[VHRED]]''' ||  || [[Variational Hierarchical Recurrent Encoder-Decoder]]
|-
| '''[[VISSIM]]''' ||  || [[A traffic simulation software (from "Verkehr In Städten]]
|-
| '''[[ViT]]''' ||  || [[Vision Transformer]]
|-
| '''[[VPNN]]''' ||  || [[Vector Product Neural Network]]
|-
| '''[[VQ-VAE]]''' ||  || [[Vector Quantized Variational Autoencoders]]
|-
| '''[[VQE]]''' ||  || [[Variational Quantum Eigensolver]]
|-
|-
| '''[[VR]]''' ||  || [[Virtual reality]]
| '''[[VR]]''' ||  || [[Virtual reality]]
|-
|-
| '''[[ViT]]''' ||  || [[Vision Transformer]]
| '''[[VRP]]''' ||  || [[Vehicle Routing Problem]]
|-
| '''[[VUI]]''' ||  || [[Voice User Interface]]
|-
| '''[[WCSP]]''' ||  || [[Weighted Constraint Satisfaction Problem]]
|-
| '''[[WER]]''' ||  || [[Word Error Rate]]
|-
| '''[[WFST]]''' ||  || [[Weighted finite-state transducer (WFST)]]
|-
| '''[[WGAN]]''' ||  || [[Wasserstein Generative Adversarial Network]]
|-
| '''[[WMA]]''' ||  || [[Weighted Majority Algorithm]]
|-
| '''[[WPE]]''' ||  || [[Weighted Prediction Error]]
|-
| '''[[XAI]]''' ||  || [[Explainable Artificial Intelligence]]
|-
| '''[[XGBoost]]''' ||  || [[eXtreme Gradient Boosting]]
|-
| '''[[XOR]]''' ||  || [[Exclusive OR (a common problem in neural networks)]]
|-
| '''[[YOLO]]''' ||  || [[You Only Look Once]]
|-
| '''[[ZSL]]''' ||  || [[Zero-Shot Learning]]
|-
|-
|}
|}


[[Category:Guides]]
[[Category:Guides]]

Latest revision as of 04:37, 2 August 2023

See also: Guides, Terms and Abbreviations
A* A* Search Algorithm
A/B Testing A statistical method for comparing two or more treatments or algorithms
A3C Asynchronous Advantage Actor-Critic
ABAC Attribute-Based Access Control
ACE Alternating conditional expectation algorithm
ACO Ant Colony Optimization
AdA Adaptive Agent
Adam Adaptive Moment Estimation
ADASYN Adaptive Synthetic Sampling
ADT Automatic Drum Transcription
AE Autoencoder
AGC Adaptive Gradient Clipping
AGI Artificial general intelligence
AI Artificial intelligence
AIaaS Artificial Intelligence as a Service
AIWPSO Adaptive Inertia Weight Particle Swarm Optimization
AL Active Learning
AM Activation maximization
AMR Abstract Meaning Representation
AMT Automatic Music Transcription
ANI Artificial Narrow Intelligence
ANN Artificial neural network
ANOVA Analysis of variance
API Application Programming Interface
AR Augmented reality
ARNN Anticipation Recurrent Neural Network
ASI Artificial superintelligence
ASIC Application-Specific Integrated Circuit
ASR Automatic speech recognition
AST Automated speech translation
AUC Area Under the Curve
AutoML Automated Machine Learning
BB84 A quantum key distribution protocol (named after its inventors, Bennett and Brassard, and the year 1984)
BBO Biogeography-Based Optimization
BCE Binary cross-entropy
BDT Boosted Decision Tree
BERT Bidirectional Encoder Representations from Transformers
BFS Breadth-First Search
BI Business Intelligence
BiFPN Bidirectional Feature Pyramid Network
BILSTM Bidirectional Long Short-Term Memory
BLEU Bilingual evaluation understudy
BN Bayesian Network
BNN Bayesian Neural Network
BO Bayesian Optimization
BP Backpropagation
BPE Byte Pair Encoding
BPMF Bayesian Probabilistic Matrix Factorization
BPN Backpropagation Neural Network
BPTT Backpropagation through time
BQML Big Query Machine Learning
BR Best-Response (in game theory)
BRDF Bidirectional reflectance distribution function
BRNN Bidirectional Recurrent Neural Network
BRR Bayesian ridge regression
CAD Computer-Aided Design
CAE Contractive Autoencoder
CALA Continuous Action-set Learning Automata
CAM Computer-Aided Manufacturing
CAPTCHA Completely Automated Public Turing test to tell Computers and Humans Apart
CART Classification And Regression Tree
CASE Computer-Aided Software Engineering
CatBoost Categorical Boosting
CAV Concept Activation Vectors
CBAC Content-Based Access Control
CBI Counterfactual Bias Insertion
CBOW Continuous Bag of Words
CBR Case-Based Reasoning
CCA Canonical Correlation Analysis
CCC Canonical Correlation Coefficients
CCE Categorical cross-entropy
CDBN Convolutional Deep Belief Networks
CE Cross-Entropy
CEC Constant Error Carousel
CEGAR Counterexample-Guided Abstraction Refinement
CEGIS Counterexample-Guided Inductive Synthesis
CF Common Features
cGAN Conditional Generative Adversarial Network
CL Confident learning
CLIP Contrastive Language-Image Pre-Training
CLNN ConditionaL Neural Networks
CMA Covariance Matrix Adaptation
CMA-ES Covariance Matrix Adaptation Evolution Strategy
CMAC Cerebellar Model Articulation Controller
CMMs Conditional Markov Model
CNN Convolutional neural network
COIN-OR Computational Infrastructure for Operations Research
ConvNet Convolutional Neural Network
COT Chain of Thought
COTE Collective of Transformation-Based Ensembles
COTP Chain of Thought Prompting
CP Constraint Programming
CPLEX An optimization solver (from "C" programming language and "simplex")
CPN Colored Petri Nets
CRBM Conditional Restricted Boltzmann Machine
CRF Conditional Random Field
CRFs Conditional Random Fields
CRNN Convolutional Recurrent Neural Network
CSLR Continuous Sign Language Recognition
CSP Constraint Satisfaction Problem
CSV Comma-separated values
CT-LSTM Convolutional Transformer Long Short-Term Memory
CTC Connectionist Temporal Classification
CTR Collaborative Topic Regression
CUDA Compute Unified Device Architecture
CV Computer Vision, Cross validation, Coefficient of variation
Cyc CycL and OpenCyc, a knowledge representation and reasoning system
D* Dynamic A* Search Algorithm
DAAF Data Augmentation and Auxiliary Feature
DaaS Data as a Service
DAE Denoising AutoEncoder or Deep AutoEncoder
DAML DARPA Agent Markup Language
DART Disturbance Aware Regression Tree
DBM Deep Boltzmann Machine
DBN Deep belief network
DBSCAN Density-Based Spatial Clustering of Applications with Noise
DCAI Data-centric AI
DCGAN Deep Convolutional Generative Adversarial Network
DCMDN Deep Convolutional Mixture Density Network
DDPG Deep Deterministic Policy Gradient
DE Differential evolution
DeconvNet DeConvolutional Neural Network
DeepLIFT Deep Learning Important FeaTures
DFS Depth-First Search
DL Deep learning
DM Diffusion model
DNN Deep neural network
DP Dynamic Programming
DQN Deep Q-Learning
DR Detection Rate
DRL Deep Reinforcement Learning
DS Data Science
DSN Deep Stacking Network
DSR Deep Symbolic Reinforcement Learning
DSS Decision Support System
DSW Data Stream Warehousing
DT Decision Tree
DTD Deep Taylor Decomposition
DWT Discrete Wavelet Transform
EDA Exploratory data analysis
EKF Extended Kalman Filter
ELECTRA Efficiently Learning an Encoder that Classifies Token Replacements Accurately
ELM Extreme Learning Machine
ELMo Embeddings from Language Models
ELU Exponential Linear Unit
EM Expectation maximization
EMD Entropy Minimization Discretization
ERNIE Enhanced Representation through kNowledge IntEgration
ES Evolution Strategies
ESN Echo State Network
ETL Extract, Transform, Load
ETL Pipeline Extract Transform Load Pipeline
EXT Extremely Randomized Trees
F1 F1 Score (harmonic mean of precision and recall)
F1 Score Harmonic Precision-Recall Mean
FALA Finite Action-set Learning Automata
Fast R-CNN Faster Region-based Convolutional Neural Network
FC Fully-Connected
FC-CNN Fully Convolutional Convolutional Neural Network
FC-LSTM Fully Connected Long Short-Term Memory
FCM Fuzzy C-Means
FCN Fully Convolutional Network
FER Facial Expression Recognition
FFT Fast Fourier transform
FL Federated Learning
FLOP Floating Point Operations
FLOPS Floating Point Operations Per Second
FM Foundation model
FN False negative
FNN Feedforward Neural Network
FNR False negative rate
FOAF Friend of a Friend (ontology)
FP False positive
FPGA Field-Programmable Gate Array
FPN Feature Pyramid Network
FPR False positive rate
FST Finite state transducer
FTL Few-Shot Learning
FWA Fireworks Algorithm
FWIoU Frequency Weighted Intersection over Union
GA Genetic Algorithm
GALE Global Aggregations of Local Explanations
GAM Generalized Additive Model
GAN Generative Adversarial Network
GAP Global Average Pooling
GBDT Gradient Boosted Decision Tree
GBM Gradient Boosting Machine
GBRCN Gradient-Boosting Random Convolutional Network
GD Gradient descent
GEBI Global Explanation for Bias Identification
GFNN Gradient frequency neural network
GLCM Gray Level Co-occurrence Matrix
GLM Generalized Linear Model
GLOM A neural network architecture by Geoffrey Hinton
Gloss2Text A task of transforming raw glosses into meaningful sentences.
GloVE Global Vectors
GLPK GNU Linear Programming Kit
GLUE General Language Understanding Evaluation
GMM Gaussian mixture model
GP Genetic Programming
GPR Gaussian process regression
GPT Generative Pre-Training
GPU Graphics processing unit
GradCAM GRADient-weighted Class Activation Mapping
GRU Gated recurrent unit
Gurobi An optimization solver (named after its founders, Zonghao Gu, Edward Rothberg, and Robert Bixby)
HamNoSys Hamburg Sign Language Notation System
HAN Hierarchical Attention Networks
HC Hierarchical Clustering
HCA Hierarchical Clustering Analysis
HDP Hierarchical Dirichlet process
HF Hugging Face
HHDS HipHop Dataset
hLDA Hierarchical Latent Dirichlet allocation
HMM Hidden Markov Model
HNN Hopfield Neural Network
HOG Histogram of Oriented Gradients (feature descriptor)
Hopfield Hopfield Network
HPC High Performance Computing
HRED Hierarchical Recurrent Encoder-Decoder
HRI Human-Robot Interaction
HSMM Hidden Semi-Markov Model
HTM Hierarchical Temporal Memory
i.i.d Independent and Identically Distributed
i.i.d. Independently and identically distributed
IaaS Infrastructure as a Service
ICA Independent component analysis
ICP Iterative Closest Point (point cloud registration)
ID3 Iterative Dichotomiser 3
IDA* Iterative Deepening A* Search Algorithm
IDR Input dependence rate
IG Invariant Generation
IID Independently and identically distributed
IIR Input independence rate
ILASP Inductive Learning of Answer Set Programs
ILP Integer Linear Programming
INFD Explanation Infidelity
IoA Internet of Agents
IoE Internet of Everything
IoT Internet of Things
IoU Jaccard index (intersection over union)
IR Information Retrieval
IRCoT Interleaving Retrieval CoT
ISIC International Skin Imaging Collaboration
IVR Interactive Voice Response
K-Means K-Means Clustering
KB Knowledge Base
KDE Kernel Density Estimation
KF Kalman Filter
kFCV K-fold cross validation
KL Kullback Leibler (KL) divergence
KNN K-nearest neighbors
KR Knowledge Representation
KRR Kernel Ridge Regression
LAION Large-scale Artificial Intelligence Open Network
LAMA LAnguage Model Analysis
LaMDA Language Models for Dialog Applications
LBP Local Binary Pattern (texture descriptor)
LDA Latent Dirichlet Allocation
LDADE Latent Dirichlet Allocation Differential Evolution
LEPOR Language Evaluation Portal
LightGBM Light Gradient Boosting Machine
LIME Local Interpretable Model-agnostic Explanations
LINGO A software for linear, nonlinear, and integer optimization
LL Lifelong learning
LLM Large language model
LLS Linear least squares
LMNN Large Margin Nearest Neighbor
LoLM Lots of Little Models
LP Linear Programming
LPAQA Language model Prompt And Query Archive
LRP Layer-wise Relevance Propagation
LSA Latent semantic analysis
LSI Latent Semantic Indexing
LSTM Long short-term memory
LSTM-CRF Long Short-Term Memory with Conditional Random Field
LTR Learning To Rank
LVQ Learning Vector Quantization
M2M Machine to Machine
MADE Masked Autoencoder for Distribution Estimation
MAE Mean absolute error
MAF Masked Autoregressive Flows
MAIRL Multi-Agent Inverse Reinforcement Learning
MAP Maximum A Posteriori (MAP) Estimation
MAPE Mean absolute percentage error
MARL Multi-Agent Reinforcement Learning
MART Multiple Additive Regression Tree
MaxEnt Maximum Entropy
MAXSAT Maximum Satisfiability Problem
MCLNN Masked ConditionaL Neural Networks
MCMC Markov Chain Monte Carlo
MCS Model contrast score
MCTS Monte Carlo Tree Search
MDL Minimum description length (MDL) principle
MDN Mixture Density Network
MDP Markov Decision Process
MDRNN Multidimensional recurrent neural network
MER Music Emotion Recognition
METEOR Metric for Evaluation of Translation with Explicit ORdering
MIL Multiple Instance Learning
MILP Mixed-Integer Linear Programming
MINT Mutual Information based Transductive Feature Selection
MIoU Mean Intersection over Union
MIP Mixed-Integer Programming
ML Machine learning
MLaaS Machine Learning as a Service
MLE Maximum Likelihood Estimation
MLLM Multimodal large language model
MLM Music Language Models
MLP Multi-Layer Perceptron
MMI Maximum Mutual Information
MNIST Modified National Institute of Standards and Technology
MOEA Multi-Objective Evolutionary Algorithm
MPA Mean Pixel Accuracy
MR Mixed Reality
MRF Markov Random Field
MRR Mean Reciprocal Rank
MRS Music Recommender System
MSDAE Modified Sparse Denoising Autoencoder
MSE Mean squared error
MSR Music Style Recognition
MTL Multi-Task Learning
NARX Nonlinear AutoRegressive with eXogenous input (neural network model)
NAS Neural Architecture Search
NB Na ̈ıve Bayes
NBKE Na ̈ıve Bayes with Kernel Estimation
NDCG Normalized Discounted Cumulative Gain
NE Nash Equilibrium (in game theory)
NEAT NeuroEvolution of Augmenting Topologies
NER Named entity recognition
NERQ Named Entity Recognition in Query
NEST Neural Simulation Tool
NF Normalizing Flow
NFL No Free Lunch (NFL) theorem
NISQ Noisy Intermediate-Scale Quantum (quantum computing)
NLG Natural Language Generation
NLP Natural Language Processing
NLT Neural Machine Translation
NLU Natural Language Understanding
NMF Non-negative matrix factorization
NMS Non Maximum Suppression
NMT Neural Machine Translation
NN Neural network
NNMODFF Neural Network based Multi-Onset Detection Function Fusion
NPE Neural Physical Engine
NRMSE Normalized RMSE
NSGA-II Non-dominated Sorting Genetic Algorithm II
NST Neural style transfer
NTM Neural Turing Machine
NuSVC Nu-Support Vector Classification
NuSVR Nu-Support Vector Regression
OBM One Big Model
OCR Optical character recognition
OD Object Detection
ODF Onset Detection Function
OIL Ontology Inference Layer
OLR Ordinary Linear Regression
OLS Ordinary Least Squares
OMNeT++ Objective Modular Network Testbed in C++
OMR Optical Mark Recognition
OOF Out-of-fold
ORB Oriented FAST and Rotated BRIEF (feature descriptor)
OWL Web Ontology Language
PA Pixel Accuracy
PaaS Platform as a Service
PACO Poisson Additive Co-Clustering
PaLM Pathways Language Model
PBAC Policy-Based Access Control
PCA Principal component analysis
PCL Point Cloud Library (3D perception)
PECS Physics Engine for Collaborative Simulation
PEGASUS Pre-training with Extracted Gap-Sentences for Abstractive Summarization
PF Particle Filter
PFE Probabilistic facial embedding
PLSI Probabilistic Latent Semantic Indexing
PM Project Manager
PMF Probabilistic Matrix Factorization
PMI Pointwise Mutual Information
PNN Probabilistic Neural Network
POC Proof of Concept
POMDP Partially Observable Markov Decision Process
POS Part of Speech (POS) Tagging
POT Partially Observable Tree (decision-making under uncertainty)
PPL Perplexity (a measure of language model performance)
PPMI Positive Pointwise Mutual Information
PPO Proximal Policy Optimization
PReLU Parametric Rectified Linear Unit-Yor Topic Modeling
PRM Probabilistic Roadmap (motion planning algorithm)
PSO Particle Swarm Optimization
PU Positive Unlabaled
PYTM Pitman
QA Question Answering
QAOA Quantum Approximate Optimization Algorithm
QAP Quadratic Assignment Problem
QEC Quantum Error Correction
QFT Quantum Fourier Transform
QIP Quantum Information Processing
QKD Quantum Key Distribution
QML Quantum Machine Learning
QNN Quantum Neural Network
QP Quadratic Programming
QPE Quantum Phase Estimation
R-CNN Region-based Convolutional Neural Network
R2 R-squared
RandNN Random Neural Network
RANSAC RANdom SAmple Consensus
RBAC Rule-Based Access Control
RBF Radial Basis Function
RBFNN Radial Basis Function Neural Network
RBM Restricted Boltzmann Machine
RDF Resource Description Framework
ReAct Reason + Act
REALM Retrieval-Augmented Language Model Pre-Training
ReCAPTCHA Reverse CAPTCHA
ReLU Rectified Linear Unit
REPTree Reduced Error Pruning Tree
RETRO Retrieval Enhanced Transformer
RF Random forest
RFE Recursive Feature Elimination
RGB Red Green Blue color model
RICNN Rotation Invariant Convolutional Neural Network
RIM Recurrent Interence Machines
RIPPER Repeated Incremental Pruning to Produce Error Reduction
RISE Random Interval Spectral Ensemble
RL Reinforcement learning
RLFM Regression based latent factors
RLHF Reinforcement Learning from Human Feedback
RMSE Root mean squared error
RMSLE Root mean squared logarithmic error
RMSprop Root Mean Square Propagation
RNN Recurrent neural network
RNNLM Recurrent Neural Network Language Model (RNNLM)
RoBERTa Robustly Optimized BERT Pretraining Approach
ROC Receiver operating characteristic
ROI Region Of Interest
ROS Robot Operating System
ROUGE Recall-Oriented Understudy for Gisting Evaluation (NLP metric)
RPA Robotic Process Automation
RR Ridge Regression
RRT Rapidly-exploring Random Tree (motion planning algorithm)
RSI Recursive self-improvement
RTRL Real-Time Recurrent Learning
SA Simulated Annealing, Segment Anything
SAM Segment Anything Model
SaaS Software as a Service
SAC Soft Actor-Critic
SAE Stacked AE
SARSA State-Action-Reward-State-Action
SAT Satisfiability Problem
SBAC Situation-Based Access Control
SBM Stochastic block model
SBO Structured Bayesian optimization
SBSE Search-based software engineering
SCH Stochastic convex hull
SCIP Solving Constraint Integer Programs
SDAE Stacked DAE
seq2seq Sequence to Sequence Learning
SER Sentence Error Rate
SGBoost Stochastic Gradient Boosting
SGD Stochastic gradient descent
SGVB Stochastic Gradient Variational Bayes
SHAP SHapley Additive exPlanation
SHLLE Supervised Hessian Locally Linear Embedding
SIFT Scale-Invariant Feature Transform (feature detection)
Sign2(Gloss+Text) Sign to Gloss and Text
Sign2Gloss A one to one translation from the single sign to the single gloss.
Sign2Text A task of full translation from the sign language into the spoken one
SL Supervised learning
SLAM Simultaneous Localization and Mapping
SLDS Switching Linear Dynamical System
SLP Single-Layer Perceptron
SLRT Sign Language Recognition Transformer
SLT Sign Language Translation
SLTT Sign Language Translation Transformer
SMA* Simplified Memory-bounded A* Search Algorithm
SMBO Sequential Model-Based Optimization
SMO Sequential Minimal Optimization
SMOTE Synthetic Minority Over-sampling Technique
SNN Sparse Neural Network
SOM Self-Organizing Map
SOTA State of the Art
SPARQL SPARQL Protocol and RDF Query Language
Spiking NN Spiking Neural Network
SPM SentencePiece Model (subword tokenization)
SpRay Spectral Relevance Analysis
SSD Single-Shot Detector
SSL Self-Supervised Learning
SSVM Smooth support vector machine
ST Style transfer
STaR Self-Taught Reasoner
STDA Style Transfer Data Augmentation
STDP Spike Timing-Dependent Plasticity
STL Selt-Taught Learning
SUMO Simulation of Urban Mobility
SURF Speeded-Up Robust Features (feature detection)
SVC Support Vector Classification
SVD Singing Voice Detection
SVM Support vector machine
SVR Support Vector Regression
SVS Singing Voice Separation
SWI-Prolog Semantic Web Interface for Prolog
t-SNE t-distributed stochastic neighbor embedding
T5 Text-To-Text Transfer Transformer
TD Temporal Difference
TDA Targeted Data Augmentation
TDE Time Domain Ensemble
tf-idf term frequency–inverse document frequency
TGAN Temporal Generative Adversarial Network
THAID THeta Automatic Interaction Detection
TINT Tree-Interpreter
TL Transfer Learning
TLFN Time-Lagged Feedforward Neural Network
TN True negative
TNR True negative rate
ToM Theory of Mind
TP True positive
TPOT Tree-based Pipeline Optimization Tool
TPR True positive rate
TPU Tensor Processing Unit
TRPO Trust Region Policy Optimization
TS Tabu Search
TSF Time Series Forest
TSP Traveling Salesman Problem
TTS Text-to-Speech
UCT Upper Confidence bounds applied to Trees (Monte Carlo Tree Search variant)
UDA Unsupervised Data Augmentation
UKF Unscented Kalman Filter
UL Unsupervised learning
ULMFiT Universal Language Model Fine-Tuning
UMAP Uniform Manifold Approximation and Projection
USM Universal Speech Model
V-Net Volumetric Convolutional neural network
VAD Voice Activity Detection
VAE Variational AutoEncoder
VGG Visual Geometry Group
VHRED Variational Hierarchical Recurrent Encoder-Decoder
VISSIM A traffic simulation software (from "Verkehr In Städten
ViT Vision Transformer
VPNN Vector Product Neural Network
VQ-VAE Vector Quantized Variational Autoencoders
VQE Variational Quantum Eigensolver
VR Virtual reality
VRP Vehicle Routing Problem
VUI Voice User Interface
WCSP Weighted Constraint Satisfaction Problem
WER Word Error Rate
WFST Weighted finite-state transducer (WFST)
WGAN Wasserstein Generative Adversarial Network
WMA Weighted Majority Algorithm
WPE Weighted Prediction Error
XAI Explainable Artificial Intelligence
XGBoost eXtreme Gradient Boosting
XOR Exclusive OR (a common problem in neural networks)
YOLO You Only Look Once
ZSL Zero-Shot Learning