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.
In this paper, we propose EFANNA, an extremely fast approximate nearest neighbor search algorithm based on $k$NN Graph.
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).
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.
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.
#3 best model for Skeleton Based Action Recognition on NTU RGB+D
Deep NLP models benefit from underlying structures in the data---e. g., parse trees---typically extracted using off-the-shelf parsers.
Our data is the first dataset for inter-personal relationship extraction.
Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods.
SOTA for Graph Classification on IPC-grounded