graph construction
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Greatest papers with code
DyNet: The Dynamic Neural Network Toolkit
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.
Visualizing Large-scale and High-dimensional Data
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.
EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph
In this paper, we propose EFANNA, an extremely fast approximate nearest neighbor search algorithm based on $k$NN Graph.
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
ACL 2019
• atcbosselut/comet-commonsense
• 
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).
Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
• lshiwjx/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.
#2 best model for
Skeleton Based Action Recognition
on Kinetics-Skeleton dataset
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.
#8 best model for
Skeleton Based Action Recognition
on Kinetics-Skeleton dataset
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.
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.
Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection
• GXYM/DRRG
•
In this paper, we propose a novel unified relational reasoning graph network for arbitrary shape text detection.
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.
#4 best model for
Joint Entity and Relation Extraction
on SciERC
COREFERENCE RESOLUTION GRAPH CONSTRUCTION JOINT ENTITY AND RELATION EXTRACTION NAMED ENTITY RECOGNITION