[PDF] Top 20 Graph to Sequence Learning using Gated Graph Neural Networks
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Graph to Sequence Learning using Gated Graph Neural Networks
... when using an initial RNN layer in their ...Levi graph transformation. Figure 4 shows an example of an input graph for g2s+, with the additional sequential edges connecting the words (reverse and ... See full document
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Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks
... supervised learning on ...in graph kernels have emphasized scalability, focusing on techniques that bypass expensive Gram matrix computations by using explicit feature ...deep graph kernels ... See full document
8
Sequence-to-sequence modeling for graph representation learning
... LSTM sequence-to-sequence learning framework of (Sutskever et ...input sequence into a vector and another LSTM to generate the output sequence from that ...same sequence as both ... See full document
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Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing
... and using LSTM as the baseline model, the proposed approach is applicable for other domain tasks as well, with more complicated deep learning mod- els as ... See full document
10
Graph Neural Networks with Generated Parameters for Relation Extraction
... pose graph neural networks with generated pa- rameters (GP-GNNs), to adapt graph neural net- works to solve the natural language relational rea- soning ...connected graph with ... See full document
9
Graph Enhanced Cross Domain Text to SQL Generation
... deep learning approaches for seman- tic parsing have shown promise on a variety of benchmark data sets, particularly on text- to-SQL ...cross-domain learning scheme to per- form text-to-SQL translation and ... See full document
5
Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks
... deep learning into Hawkes processes, which aims to efficiently capture mean- ingful patterns in a large collection of correlated sequences of recurrent ...each sequence is modeled as a Hawkes process and ... See full document
8
Attributed Graph Classification via Deep Graph Convolutional Neural Networks
... A graph or network represents these relationships ...cial networks, biological networks, chemical networks, citation networks, and research networks, among ...of graph ... See full document
124
Hypergraph Neural Networks
... hypergraph neural networks framework (HGNN) for data representation ...Traditional graph convolutional neural networks can be regarded as a special case of ...with graph ... See full document
8
End to End Graph Based TAG Parsing with Neural Networks
... parsing using the same feature representations from the ...multi-task learning frame- work further improves performance in all three ...multi-task learning yields feature representations in the LSTM ... See full document
14
Learning Sequence Encoders for Temporal Knowledge Graph Completion
... a sequence of predicate tokens that always consists of the relation type token and, if available, a tem- poral modifier token such as “since” or ...token sequence and (if available) the sequence of ... See full document
6
Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions
... convolutional neural networks (GCNs) (Kipf and Welling, 2017) and attention-based neural sequence labeling (Tan et ...global graph structure for the entire ...for learning ... See full document
7
Session-Based Recommendation with Graph Neural Networks
... Neural-network-based methods, such as NARM and STAMP, outperform the conventional methods, demon- strating the power of adopting deep learning in this do- main. Short/long-term memory models, like GRU4REC ... See full document
8
Value-based argumentation frameworks as neural-symbolic learning systems
... a neural network can be organised in ...acyclic graph. It consists of a sequence of layers and connections between successive layers, containing one input layer, n − 2 hidden layers, and one output ... See full document
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Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification
... convolutional neural net- works to graph-structured data have emerged. Graph con- volutional neural networks (GCNNs) have been used to ad- dress node and graph classification and ... See full document
8
Gated Residual Recurrent Graph Neural Networks for Traffic Prediction
... deep neural network based methods such as (iv) FNN: Feed forward neural network with two hidden lay- ers; (v) FC-LSTM: Fully connected LSTM neural networks; (vi) DCRNN: MRes-RGNN (Li et ... See full document
8
Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures
... machine learning approaches (with manually designed features) (Diab et ...deep learning has been applied to solve ...deep learning has also been considered for the related tasks of EFP, including ... See full document
7
AutoNet: Knowledge Graphs for Occasions Object Recognition
... deep learning for image recognition, recent works have explored deep convolutional neural networks (CNN) for image ...description learning was first proposed in and shows that this theme is ... See full document
9
GeniePath: Graph Neural Networks with Adaptive Receptive Paths
... We demonstrate the idea of learning receptive paths of graph neural networks in Figure 2. Instead of aggregating all the 2-hops neighbors to calculate the embedding of the tar- get node ... See full document
8
Transition based Dependency Parsing Using Two Heterogeneous Gated Recursive Neural Networks
... Recently, neural network based depen- dency parsing has attracted much interest, which can effectively alleviate the prob- lems of data sparsity and feature engineer- ing by using the dense ...in ... See full document
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