The sequence graph G with new labels
Sequence-to-sequence modeling for graph representation learning
... Figure 12 shows the comparison between different unsupervised models, using the sequences of first order neighborhoods. Model S2S-AE-PP is better than Model S2S-AE in nearly all cases. As we conjectured above, Model ...
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The transformation graph G ++−
... complete graph of order ...bipartite graph with two partite sets containing r and s ...the graph obtained from K 1,s by adding a new edge which joins two vertices of degrees ...graphs G ...
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Aggregating and Predicting Sequence Labels from Crowd Annotations
... ‘easy words’ labeled correctly by all workers. More recently, ? proposed HMM models for aggregating crowdsourced discourse segmentation labels. However, they did not consider the general sequence labeling ...
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Densely Connected Graph Convolutional Networks for Graph to Sequence Learning
... Levi graph. 3.3 Direction Aggregation Directionality and edge labels play an important role in linguistic ...different labels should have different weight matrices, ...AMR graph we have four ...
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Graph to Sequence Learning using Gated Graph Neural Networks
... Levi Graph Transformation The g2s model proposed in §2 has two key defi- ...edge labels into a single one (Marcheggiani and Titov, 2017; Bast- ings et ...
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Learning a Propagable Graph for Classification under the Scenario with Uncertain Labels
... Data mining is the technique to extract features from raw data. It is used as a basic of knowledge discovery of data. Data mining is commonly used in areas like genetics, education, finance, bio-informatics, pattern ...
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AMR Parsing as Sequence to Graph Transduction
... On the training set, we use simple rules to find mappings between anonymized sub-graphs and spans of text, and then replace mapped text with the anonymized token we inserted into the AMR graph. Additionally, we ...
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Predicting a Switching Sequence of Graph Labelings
... the graph labeling setting as the number of needed experts is 2 n for an n-vertex graph, and computing the marginal probabilities with a natural prior ...a graph even without switching is a ...
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Graph editing to a given degree sequence.
... the graph editing problem called Editing to a Graph with a Given Degree Sequence where the aim is to obtain a graph with a given degree se- quence σ by at most k vertex or edge deletions and ...
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A Graph to Sequence Model for AMR to Text Generation
... original graph segment “p / pro- vide :ARG0 (a / agree)”, which indicates that “a / agree” is the subject of “p / ...neighboring graph nodes, or distant graph nodes, which prevents the decoder from ...
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Graph Invariants of Trees with Given Degree Sequence
... X uv∈E(T ) f (d(u), d(v)). We first introduce the extremal tree structures, with a given degree sequence, that maximize or minimize such functions under certain conditions. When a partial order- ing, called ...
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GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention
... within graph- based time-dependent ...for graph-based time- dependent data with numerical values, data at consecutive time in- stants may be very different, especially when non-stationarity ...
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Generating Knowledge Graph Paths from Textual Definitions using Sequence to Sequence Models
... Our model is based on a sequence-to-sequence neural network (Sutskever et al., 2014) coupled with an attention mechanism (Bahdanau et al., 2014). Specifically, we use an LSTM (Hochre- iter and Schmidhuber, ...
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Generating knowledge graph paths from textual definitions using sequence-to-sequence models
... knowledge graph (KG) or an ontology (we use two terms, interchange- ...a sequence of its ancestors in the ontology (hy- pernyms) and then maps the corresponding textual description to this unique ...
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Learning Sequence Encoders for Temporal Knowledge Graph Completion
... ’Make a visit’ or ’Express intent to meet or ne- gotiate’). Additional information can be found at http://www.icews.com/ . The repository is organized in dumps that contain the events that occurred each year from 1995 to ...
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Cover time of a random graph with given degree sequence
... ) = O (ζ ω ). We now turn to the case where G v contains a unique light cycle C . Let x be the furthest vertex of C from v in G v . This is the only possible place where the random walk is more likely to ...
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The scaling window for a random graph with a given degree sequence
... degree sequence exhibits a power law. (An inhomo- geneous random graph is one in which the edges be- tween pairs of vertices are chosen independently, but with varying ...
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New Fuel Economy and Environment Labels for a New Generation of Vehicles
... information like local gas prices along with individual driving habits to get best possible cost and energy-use estimates. EPA and NHTSA conducted extensive research to inform the development of this new label. ...
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Multi-label learning with emerging new labels
... Emerging New Labels Yue Zhu, Kai Ming Ting, and Zhi-Hua Zhou, Fellow, IEEE Abstract—In a multi-label learning task, an object possesses multiple concepts where each concept is represented by a class ...
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Social stream classification with emerging new labels
... identify new labels as they ...emerging new labels, and propose a novel ensemble framework, integrating an instance- based learner and a label-based learner by completely-random ...known ...
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