Definition
A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.
Related concepts
AAAI Conference on Artificial IntelligenceACG distributionActive learning (machine learning)AlexNetAmbient isotopyAnomaly detectionApprenticeship learningArtificial neural networkAssociation rule learningAutoencoderAutomated machine learningAutomatic differentiationBIRCHBatch learningBayesian networkBias–variance tradeoffBijectionBoltzmann machineBoosting (machine learning)Bootstrap aggregatingCURE algorithmCanonical correlationCategorical distributionChain ruleChelsea FinnCluster analysisCoefficient of determinationComputational learning theoryConditional random fieldConference on Neural Information Processing SystemsConfusion matrixConvolutional neural networkCrowdsourcingCurriculum learningDBSCANData cleaningData miningDecision tree learningDeepDreamDeep learningDensity estimationDeterminantDiffeomorphismDiffusion modelDimensionality reductionDirichlet distributionDiscriminative modelECML PKDDEcho state networkEigenvectorElectrochemical RAMEmpirical risk minimizationEnsemble learningExpectation–maximization algorithmFactor analysisFeature engineeringFeature learningFeedforward neural networkFuzzy clusteringGated recurrent unitGenerative adversarial networkGenerative modelGlossary of artificial intelligenceGram determinantGrammar inductionGraphical modelGroup mathematicsHidden Markov modelHierarchical clusteringHomeomorphismHuman-in-the-loopImportance samplingIndependent component analysisInternational Conference on Learning RepresentationsInternational Conference on Machine LearningInternational Joint Conference on Artificial IntelligenceInverse functionInverse function theoremInvertible matrixIsolation forestIsometryJacobian matrix and determinantJournal of Machine Learning ResearchK-means clusteringK-nearest neighbors algorithmKernel machinesKullback–Leibler divergenceLeNetLearning curve (machine learning)Learning to rankLebesgue measureLeft inverse functionLinear algebraLinear discriminant analysisLinear regressionList of datasets for machine-learning researchList of datasets in computer vision and image processingLocal outlier factorLogistic regressionLong short-term memoryLoss functionMachine Learning (journal)Machine learningMamba (deep learning architecture)Mathematical inductionMatrix determinant lemmaMaximum likelihood estimationMean shiftMechanistic interpretabilityMemtransistorMeta-learning (computer science)Multi-agent reinforcement learningMultimodal learningN-sphereNaive Bayes classifierNeural ODENeural fieldNeural network (machine learning)Neural radiance fieldNeuro-symbolic AINeuromorphic engineeringNon-negative matrix factorizationOPTICS algorithmOccam learningOnline machine learningOntology learningOptimal transportOrthogonal matrixOutline of machine learningParallelepipedPerceptronPhysics-informed neural networksPlatt scalingPolicy gradient methodPositive definite matrixPrincipal component analysisProbability densityProbability density functionProbability distributionProbably approximately correct learningProjected normal distributionProjection (linear algebra)Proper generalized decompositionPyTorchQ-learningQuadratic equationQuantum machine learningRademacher distributionRandom forestRandom sample consensusRandom variableReceiver operating characteristicRecurrent neural networkRegression analysisReinforcement learningReinforcement learning from human feedbackRelevance vector machineReparameterization trickReservoir computingRestricted Boltzmann machineRiemannian manifoldRule-based machine learningSGB distributionSelf-organizing mapSelf-play (reinforcement learning technique)Self-supervised learningSemantic analysis (machine learning)Semi-supervised learningSergey LevineSimplexSmooth manifoldSoftmax functionSparse dictionary learningSphere eversionSpiking neural networkState–action–reward–state–actionStatistical classificationStatistical learning theoryStructured predictionSupervised learningSupport vector machineT-distributed stochastic neighbor embeddingTangent spaceTemporal difference learningTopological deep learningTransformer (deep learning architecture)Typical SetU-NetUniversal approximation theoremUnsupervised learningVapnik–Chervonenkis theoryVariational autoencoderVision transformerVon Mises-Fisher distributionWhitney embedding theorem
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