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# graph construction Edit

26 papers with code · Graphs

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# DyNet: The Dynamic Neural Network Toolkit

15 Jan 2017clab/cnn

In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.

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# Visualizing Large-scale and High-dimensional Data

1 Feb 2016lferry007/LargeVis

We propose the LargeVis, a technique that first constructs an accurately approximated K-nearest neighbor graph from the data and then layouts the graph in the low-dimensional space.

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# EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph

23 Sep 2016ZJULearning/nsg

In this paper, we propose EFANNA, an extremely fast approximate nearest neighbor search algorithm based on $k$NN Graph.

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# COMET: Commonsense Transformers for Automatic Knowledge Graph Construction

We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et al., 2017).

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# Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks

15 Dec 2019lshiwjx/2s-AGCN

Second, the second-order information of the skeleton data, i. e., the length and orientation of the bones, is rarely investigated, which is naturally more informative and discriminative for the human action recognition.

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# Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition

In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing methods.

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# LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING

The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.

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# Towards Dynamic Computation Graphs via Sparse Latent Structure

Deep NLP models benefit from underlying structures in the data---e. g., parse trees---typically extracted using off-the-shelf parsers.

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# Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection

17 Mar 2020GXYM/DRRG

In this paper, we propose a novel unified relational reasoning graph network for arbitrary shape text detection.

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# Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles.

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