Jay_Tang的博客

文章目录往期文章目录链接Relation Extraction (RE)document-level REIntuitionContributionOverview of Proposed ModelProposed ModelSentence Encoding LayerGraph construction LayerNode ConstructionEdge ConstructionInference LayerFirst StepSecond StepClassification LayerResul

文章目录往期文章链接目录OverviewIntuitionPrevious WorkMajor ContributionGraph Search Neural Network (GSNN)GSNN ExplanationThree networksDiagram visualizationAdvantageIncorporate the graph network into an image pipelineDatasetConclusion往期文章链接目录往期文章链接目录OverviewThis p

Fourier TransformVirtually everything in the world can be described via a waveform - a function of time, space or some other variable. For instance, sound waves, the price of a stock, etc. The Fourier Transform gives us a unique and powerful way of viewin

Convolutional graph neural networks (ConvGNNs)Convolutional graph neural networks (ConvGNNs) generalize the operation of convolution from grid data to graph data. The main idea is to generate a node vvv’s representation byaggregating its own features xv\

Logistic Regression, L1, L2 regularization, Gradient/Coordinate descent 详细MLE v.s. MAP ~ L1, L2 Math Derivation 详细XGBoost math Derivation 通俗易懂的详细推导Introduction to Convex Optimization Basic Concept...

文章目录往期文章链接目录NoteBackground and IntuitionIntro to Graph Neural NetworksGNNs FrameworkDefinitionRecurrent graph neural networks (RecGNNs)Introduction to RecGNNsBanach's Fixed Point TheoremRecGNNs v.s. RNNsLimitation of RecGNNsGated Graph Neural Networks (GGN