Multidimensional embedding is a technique useful for characterizing spectral signature relations in hyperspectral images . However , such images consist of disjoint similar spectral classes that are spatially sensitive , thus presenting challenges to existing graph embedding methods . In this chapter , the multidimensional embedding techniques are interpreted by the potential fields method , where a sum of attraction and repulsion potential functions is minimized to find optimal energy configuration of embedding . 2 ) The semantic relationships between APIs ( e.g. , inheritances between classes , and invocations between methods ) need to be illustrated so that developers can better understand their usage scenarios . To deal with these issues , we propose GeAPI , a novel graph embedding based approach for API graph search and recommendation in this paper . First , we build a software project ’s Graph-based embedding methods receive much attention due to the use of graph and manifold information . However , conventional graph-based embedding methods may not always be effective if the data have high dimensions and have complex distributions . Testing Convexity and Acyclicity , and New Constructions for Dense Graph Embeddings This leads to the higher computational cost and curse of dimensionality . In interest of paring down the same a graph embedding framework technique isenforced wherein any dimensionality reduction algorithm can be characterized in a common framework . An image when considered as a second order tensor helps in capturing the spatial relationship between the pixels . Graph embedding , which maps graphs to a vectorial space , has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques . However , it is well known that graph embedding functions usually suffer from the loss of structural information . In this paper , we consider the hierarchical structure of a graph as a way to mitigate this loss of information . In this article , we proposed a new graph-based machine learning framework to predict the disease related genes . We first constructed a heterogeneous gene-disease association graph by integrating multiple biomedical knowledge bases , and subsequently processed the graph by a method that utilizes graph embedding representation and graph convolutional networks to learn the gene-disease associations . In our method , we defined a novel cluster loss function and a dropout mechanism to improve the generalization ability . We propose the Graph Space Embedding ( GSE ) , a technique that maps the input into a space where interactions are implicitly encoded , with little computations required . We provide theoretical results on an optimal regime for the GSE , namely a feasibility region for its parameters , and demonstrate the experimental relevance of our findings . To address this challenge , we introduce a novel general end - to - end graph - to - sequence neural encoder - decoder model that maps an input graph to a sequence of vectors and uses an attention - based LSTM method to decode the target sequence from these vectors . Our method first generates the node and graph embeddings using an improved graph - based neural network with a novel aggregation strategy to incorporate edge direction information in the node embeddings . We further introduce an attention mechanism that aligns node embeddings and the decoding sequence to better cope with large graphs . To this end , inspired by a recent graph representation learning method hamilton2017inductive , we propose an inductive graph - based neural network to learn node embeddings from node attributes through aggregation of neighborhood information for directed and undirected graphs , which explores two distinct aggregators on each node to yield two representations that are concatenated to form the final node embedding . In addition , we further design an attention - based RNN sequence decoder that takes the graph embedding as its initial hidden state and outputs a target prediction by learning to align and translate jointly based on the context vectors associated with the corresponding nodes and all previous predictions . Our code and data are available at . We propose a novel general attention - based neural networks model to elegantly address graph - to - sequence learning problems that learns a mapping between graph - structured inputs to sequence outputs , which current Seq2Seq and Tree2Seq may be inadequate to handle . We propose a novel graph encoder to learn a bi - directional node embeddings for directed and undirected graphs with node attributes by employing various aggregation strategies , and to learn graph - level embedding by exploiting two different graph embedding techniques . Equally importantly , we present an attention mechanism to learn the alignments between nodes and sequence elements to better cope with large graphs . In this section , we first introduce the node - embedding generation algorithm which derives the bi - directional node embeddings by aggregating information from both forward and backward neighborhoods of a node in a graph . Upon these node embeddings , we propose two methods for generating graph embeddings capturing the whole - graph information . subsection : Node Embedding Generation We found no significant performance difference across the three different pooling approaches ; we thus adopt the max - pooling method as our default pooling approach . Node - based Graph Embedding . In this approach , we add one super node , , into the input graph , and all other nodes in the graph direct to . Two variants of the Graph2Seq models can substantially outperform Tree2Seq , which demonstrates that a general graph to sequence model that is independent of different structural information in complex data is very useful . Interestingly , we also observe that Graph2Seq - PGE ( pooling - based graph embedding ) performs better than Graph2Seq - NGE ( node - based graph embedding ) . One potential reason is that the node - based graph embedding method artificially added a super node in graph which changes the original graph topology and brings unnecessary noise into the graph . convert graphs into sequences by following random walks on the graph then learns node embeddings using sequence - based methods . map graphs to graph vectors then classify using an output neural network . From a practical standpoint , node2vec is scalable and robust to perturbations . We showed how extensions of node embeddings to link prediction outperform popular heuristic scores designed specifically for this task . Our method permits additional binary operators beyond those listed in Table [ reference ] . This model first applies a linear projection to each tuple ( ) and then takes as input the set of projected tuples . Then , after steps of computation , the set2set model produces a graph level embedding which is invariant to the order of the of the tuples . We feed this embedding through a neural network to produce the output . We introduce the Multi - Task Graph Autoencoder ( MTGAE ) architecture , schematically depicted in Figure [ reference ] , capable of learning a shared representation of latent node embeddings from local graph topology and available explicit node features for LPNC . Our simple , yet effective and versatile model is efficiently trained end - to - end for the joint , simultaneous multi - task learning ( MTL ) of unsupervised link prediction and semi - supervised node classification in a single stage , whereas previous related deep graph embedding methods require multiple training steps that are difficult to optimize . We present an empirical evaluation of the MTGAE model on five challenging benchmark graph - structured datasets and show significant improvement in predictive performance over three strong baselines designed specifically for LPNC . For example , the Twitter followee - follower network contains 175 million active users and around twenty billion edges in 2012 . Most existing graph embedding algorithms do not scale for networks of this size . For example , the time complexity of classical graph embedding algorithms such as MDS , IsoMap , Laplacian eigenmap are at least quadratic to the number of vertices , which is too expensive for networks with millions of nodes . subsection : Graph - Based Semi - Supervised Learning A large number of approaches for semi - supervised learning using graph representations have been proposed in recent years , most of which fall into two broad categories : methods that use some form of explicit graph Laplacian regularization and graph embedding - based approaches . Prominent examples for graph Laplacian regularization include label propagation zhu2003semi , manifold regularization belkin2006manifold and deep semi - supervised embedding weston2012deep . Question Answering with Subgraph Embeddings section : The utility model provides a hinge device which comprises a hinge mechanism , a motor screw mechanism driving the hinge mechanism to operate , and a tension spring which is in a tension state all the time to supply the contraction force to the hinge mechanism . The hinge mechanism comprises a hinge body part , a hinge support and a hinge connection plate . The utility model relates to a lifting jack of a safety gear , which comprises a stander with a front wheel and a back wheel , wherein the stander is provided with a handle , a lifting hand , an oil pump , an erector arm , a long connection rod , a tray and a bracket ; the stander is provided with a safety device for preventing the erector arm from being fallen by mistake when runs or stops at high position ; the safety device comprises a supporting rod which is articulated on the stander , and a rack which is fixedly connected with the back surface of the erector arm and is inserted with the end of the supporting rod ; the supporting rod is connected with a tension spring which can be lifted up around the articulating point ; and the tension spring is connected with a pull rod which is arranged on the handle and leads the tension spring to be under the tensioning status or loosing status . The lifting jack has the following advantages : 1 . the lifting jack is convenient in use and simple in operation , and can control the safety device by manually wrenching the handle of the safety gear ; and 2 . Control valve means ( 20 ) for a fluid system , on-pointing : rests a sealing element ( 5 ) which is displaceable between an open and a closed position and on the valve body ( 8) in the closed position ; and a control element ( 2 ) , which is designed as an actuator and which is adapted to perform a reciprocating movement on a displacement path ( 1 ) ; and transmitting a tension spring element ( 3 ) which is adapted to couple the sealing element ( 5 ) with the control member ( 2 ) resiliently and the stroke of the control element ( 2 ) on the sealing element ( 5 ) . The utility model relates to a safety protective elastic connection rod device for water guide mechanism , belonging to the technical field of water guide equipment of power station . The utility model comprising connecting plates and springs is characterized in that the two connecting plates are respectively and fixedly connected with a guide vane crank and a control ring small ear handle , wherein , the two connecting plates are movably connected with each other , and a deflection spring tension element is arranged between the two connecting plates . The utility model has the advantages of simple structure , easy installation and use , low operation cost , stable performance , safe and reliable operation , so that the utility model is widely applicable for safety protection of the water guide mechanisms . Secure multi-party computation ( MPC ) enables a set of mutually distrusting parties to cooperatively compute , using a cryptographic protocol , a function over their private data . This paper presents WYS * , a new domain-specific language ( DSL ) implementation for writing MPCs . WYS * 's logic to verify the correctness and security properties of their programs . To reason about the distributed semantics of these programs , we formalize a deep embedding of WYS * , also in F*. We mechanize the necessary metatheory to prove that the properties verified for the WYS * source programs carry over to the distributed , multi-party semantics . The success of such a task heavily relies on the ability of modeling and inferring the patterns of ( or between ) the relations . In this paper , we present a new approach for knowledge graph embedding called RotatE , which is able to model and infer various relation patterns including : symmetry/antisymmetry , inversion , and composition . Specifically , the RotatE model defines each relation as a rotation from the source entity to the target entity in the complex vector space . In addition , we propose a novel self-adversarial negative sampling technique for efficiently and effectively training the RotatE model . Experimental results on multiple benchmark knowledge graphs show that the proposed RotatE model is not only scalable , but also able to infer and model various relation patterns and significantly outperform existing state-of-the-art models for link prediction . In this paper , we present a group theoretical perspective of knowledge graph embedding , connecting previous methods with different group actions . Furthermore , by utilizing Schur 's lemma from group representation theory , we show that the state of the art embedding method RotatE can model relations from any finite Abelian group . INTRODUCTION The purpose of this study was two-fold : 1 ) to investigate effects of cadence and sensitivity settings for the StepWatch ( SW3 ) on step count accuracy over a wide range of ambulatory speeds , and 2 ) to compare the preprogrammed " quick start " settings to modified settings during intermittent lifestyle activities . METHODS Part 1 : BACKGROUND There is a need to examine step-counting accuracy of activity monitors during different types of movements . The purpose of this study was to compare activity monitor and manually counted steps during treadmill and simulated free-living activities and to compare the activity monitor steps to the StepWatch ( SW ) in a natural setting . METHODS Fifteen participants performed laboratory-based treadmill ( 2.4 , 4.8 , 7.2 and 9.7 km/h ) and simulated free-living activities ( eg , cleaning room ) while wearing an activPAL , Omron HJ720-ITC , Yamax Digi- Walker SW-200 , 2 ActiGraph GT3Xs ( 1 in " low-frequency extension " [ AGLFE ] and 1 in " normal-frequency " mode ) , an ActiGraph 7164 , and a SW . In many applications , it may not be computationally feasible to search for optimal hyper-parameters . In this work , built on WatchYourStep which a graph embedding method leveraging graph attention , we propose a method that utilizes node-personalized context attention to capture the local variation in a graph structure . Specifically , we replace the shared context distribution among nodes with learnable personalized context distribution for each node .
Multidimensional embedding is a technique useful for characterizing spectral signature relations in hyperspectral images . However , such images consist of disjoint similar spectral classes that are spatially sensitive , thus presenting challenges to existing graph embedding methods . In this chapter , the multidimensional embedding techniques are interpreted by the potential fields method , where a sum of attraction and repulsion potential functions is minimized to find optimal energy configuration of embedding .
2 ) The semantic relationships between APIs ( e.g. , inheritances between classes , and invocations between methods ) need to be illustrated so that developers can better understand their usage scenarios . To deal with these issues , we propose GeAPI , a novel graph embedding based approach for API graph search and recommendation in this paper . First , we build a software project ’s
Graph-based embedding methods receive much attention due to the use of graph and manifold information . However , conventional graph-based embedding methods may not always be effective if the data have high dimensions and have complex distributions .
Testing Convexity and Acyclicity , and New Constructions for Dense Graph Embeddings
This leads to the higher computational cost and curse of dimensionality . In interest of paring down the same a graph embedding framework technique isenforced wherein any dimensionality reduction algorithm can be characterized in a common framework . An image when considered as a second order tensor helps in capturing the spatial relationship between the pixels .
Graph embedding , which maps graphs to a vectorial space , has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques . However , it is well known that graph embedding functions usually suffer from the loss of structural information . In this paper , we consider the hierarchical structure of a graph as a way to mitigate this loss of information .
In this article , we proposed a new graph-based machine learning framework to predict the disease related genes . We first constructed a heterogeneous gene-disease association graph by integrating multiple biomedical knowledge bases , and subsequently processed the graph by a method that utilizes graph embedding representation and graph convolutional networks to learn the gene-disease associations . In our method , we defined a novel cluster loss function and a dropout mechanism to improve the generalization ability .
We propose the Graph Space Embedding ( GSE ) , a technique that maps the input into a space where interactions are implicitly encoded , with little computations required . We provide theoretical results on an optimal regime for the GSE , namely a feasibility region for its parameters , and demonstrate the experimental relevance of our findings .
To address this challenge , we introduce a novel general end - to - end graph - to - sequence neural encoder - decoder model that maps an input graph to a sequence of vectors and uses an attention - based LSTM method to decode the target sequence from these vectors . Our method first generates the node and graph embeddings using an improved graph - based neural network with a novel aggregation strategy to incorporate edge direction information in the node embeddings . We further introduce an attention mechanism that aligns node embeddings and the decoding sequence to better cope with large graphs .
To this end , inspired by a recent graph representation learning method hamilton2017inductive , we propose an inductive graph - based neural network to learn node embeddings from node attributes through aggregation of neighborhood information for directed and undirected graphs , which explores two distinct aggregators on each node to yield two representations that are concatenated to form the final node embedding . In addition , we further design an attention - based RNN sequence decoder that takes the graph embedding as its initial hidden state and outputs a target prediction by learning to align and translate jointly based on the context vectors associated with the corresponding nodes and all previous predictions . Our code and data are available at .
We propose a novel general attention - based neural networks model to elegantly address graph - to - sequence learning problems that learns a mapping between graph - structured inputs to sequence outputs , which current Seq2Seq and Tree2Seq may be inadequate to handle . We propose a novel graph encoder to learn a bi - directional node embeddings for directed and undirected graphs with node attributes by employing various aggregation strategies , and to learn graph - level embedding by exploiting two different graph embedding techniques . Equally importantly , we present an attention mechanism to learn the alignments between nodes and sequence elements to better cope with large graphs .
In this section , we first introduce the node - embedding generation algorithm which derives the bi - directional node embeddings by aggregating information from both forward and backward neighborhoods of a node in a graph . Upon these node embeddings , we propose two methods for generating graph embeddings capturing the whole - graph information . subsection : Node Embedding Generation
We found no significant performance difference across the three different pooling approaches ; we thus adopt the max - pooling method as our default pooling approach . Node - based Graph Embedding . In this approach , we add one super node , , into the input graph , and all other nodes in the graph direct to .
Two variants of the Graph2Seq models can substantially outperform Tree2Seq , which demonstrates that a general graph to sequence model that is independent of different structural information in complex data is very useful . Interestingly , we also observe that Graph2Seq - PGE ( pooling - based graph embedding ) performs better than Graph2Seq - NGE ( node - based graph embedding ) . One potential reason is that the node - based graph embedding method artificially added a super node in graph which changes the original graph topology and brings unnecessary noise into the graph .
convert graphs into sequences by following random walks on the graph then learns node embeddings using sequence - based methods . map graphs to graph vectors then classify using an output neural network .
From a practical standpoint , node2vec is scalable and robust to perturbations . We showed how extensions of node embeddings to link prediction outperform popular heuristic scores designed specifically for this task . Our method permits additional binary operators beyond those listed in Table [ reference ] .
This model first applies a linear projection to each tuple ( ) and then takes as input the set of projected tuples . Then , after steps of computation , the set2set model produces a graph level embedding which is invariant to the order of the of the tuples . We feed this embedding through a neural network to produce the output .
We introduce the Multi - Task Graph Autoencoder ( MTGAE ) architecture , schematically depicted in Figure [ reference ] , capable of learning a shared representation of latent node embeddings from local graph topology and available explicit node features for LPNC . Our simple , yet effective and versatile model is efficiently trained end - to - end for the joint , simultaneous multi - task learning ( MTL ) of unsupervised link prediction and semi - supervised node classification in a single stage , whereas previous related deep graph embedding methods require multiple training steps that are difficult to optimize . We present an empirical evaluation of the MTGAE model on five challenging benchmark graph - structured datasets and show significant improvement in predictive performance over three strong baselines designed specifically for LPNC .
For example , the Twitter followee - follower network contains 175 million active users and around twenty billion edges in 2012 . Most existing graph embedding algorithms do not scale for networks of this size . For example , the time complexity of classical graph embedding algorithms such as MDS , IsoMap , Laplacian eigenmap are at least quadratic to the number of vertices , which is too expensive for networks with millions of nodes .
subsection : Graph - Based Semi - Supervised Learning A large number of approaches for semi - supervised learning using graph representations have been proposed in recent years , most of which fall into two broad categories : methods that use some form of explicit graph Laplacian regularization and graph embedding - based approaches . Prominent examples for graph Laplacian regularization include label propagation zhu2003semi , manifold regularization belkin2006manifold and deep semi - supervised embedding weston2012deep .
Question Answering with Subgraph Embeddings section :
The utility model provides a hinge device which comprises a hinge mechanism , a motor screw mechanism driving the hinge mechanism to operate , and a tension spring which is in a tension state all the time to supply the contraction force to the hinge mechanism . The hinge mechanism comprises a hinge body part , a hinge support and a hinge connection plate .
The utility model relates to a lifting jack of a safety gear , which comprises a stander with a front wheel and a back wheel , wherein the stander is provided with a handle , a lifting hand , an oil pump , an erector arm , a long connection rod , a tray and a bracket ; the stander is provided with a safety device for preventing the erector arm from being fallen by mistake when runs or stops at high position ; the safety device comprises a supporting rod which is articulated on the stander , and a rack which is fixedly connected with the back surface of the erector arm and is inserted with the end of the supporting rod ; the supporting rod is connected with a tension spring which can be lifted up around the articulating point ; and the tension spring is connected with a pull rod which is arranged on the handle and leads the tension spring to be under the tensioning status or loosing status . The lifting jack has the following advantages : 1 . the lifting jack is convenient in use and simple in operation , and can control the safety device by manually wrenching the handle of the safety gear ; and 2 .
Control valve means ( 20 ) for a fluid system , on-pointing : rests a sealing element ( 5 ) which is displaceable between an open and a closed position and on the valve body ( 8) in the closed position ; and a control element ( 2 ) , which is designed as an actuator and which is adapted to perform a reciprocating movement on a displacement path ( 1 ) ; and transmitting a tension spring element ( 3 ) which is adapted to couple the sealing element ( 5 ) with the control member ( 2 ) resiliently and the stroke of the control element ( 2 ) on the sealing element ( 5 ) .
The utility model relates to a safety protective elastic connection rod device for water guide mechanism , belonging to the technical field of water guide equipment of power station . The utility model comprising connecting plates and springs is characterized in that the two connecting plates are respectively and fixedly connected with a guide vane crank and a control ring small ear handle , wherein , the two connecting plates are movably connected with each other , and a deflection spring tension element is arranged between the two connecting plates . The utility model has the advantages of simple structure , easy installation and use , low operation cost , stable performance , safe and reliable operation , so that the utility model is widely applicable for safety protection of the water guide mechanisms .
Secure multi-party computation ( MPC ) enables a set of mutually distrusting parties to cooperatively compute , using a cryptographic protocol , a function over their private data . This paper presents WYS * , a new domain-specific language ( DSL ) implementation for writing MPCs . WYS
* 's logic to verify the correctness and security properties of their programs . To reason about the distributed semantics of these programs , we formalize a deep embedding of WYS * , also in F*. We mechanize the necessary metatheory to prove that the properties verified for the WYS * source programs carry over to the distributed , multi-party semantics .
The success of such a task heavily relies on the ability of modeling and inferring the patterns of ( or between ) the relations . In this paper , we present a new approach for knowledge graph embedding called RotatE , which is able to model and infer various relation patterns including : symmetry/antisymmetry , inversion , and composition . Specifically , the RotatE model defines each relation as a rotation from the source entity to the target entity in the complex vector space . In addition , we propose a novel self-adversarial negative sampling technique for efficiently and effectively training the RotatE model . Experimental results on multiple benchmark knowledge graphs show that the proposed RotatE model is not only scalable , but also able to infer and model various relation patterns and significantly outperform existing state-of-the-art models for link prediction .
In this paper , we present a group theoretical perspective of knowledge graph embedding , connecting previous methods with different group actions . Furthermore , by utilizing Schur 's lemma from group representation theory , we show that the state of the art embedding method RotatE can model relations from any finite Abelian group .
INTRODUCTION The purpose of this study was two-fold : 1 ) to investigate effects of cadence and sensitivity settings for the StepWatch ( SW3 ) on step count accuracy over a wide range of ambulatory speeds , and 2 ) to compare the preprogrammed " quick start " settings to modified settings during intermittent lifestyle activities . METHODS Part 1 :
BACKGROUND There is a need to examine step-counting accuracy of activity monitors during different types of movements . The purpose of this study was to compare activity monitor and manually counted steps during treadmill and simulated free-living activities and to compare the activity monitor steps to the StepWatch ( SW ) in a natural setting . METHODS Fifteen participants performed laboratory-based treadmill ( 2.4 , 4.8 , 7.2 and 9.7 km/h ) and simulated free-living activities ( eg , cleaning room ) while wearing an activPAL , Omron HJ720-ITC , Yamax Digi- Walker SW-200 , 2 ActiGraph GT3Xs ( 1 in " low-frequency extension " [ AGLFE ] and 1 in " normal-frequency " mode ) , an ActiGraph 7164 , and a SW .
In many applications , it may not be computationally feasible to search for optimal hyper-parameters . In this work , built on WatchYourStep which a graph embedding method leveraging graph attention , we propose a method that utilizes node-personalized context attention to capture the local variation in a graph structure . Specifically , we replace the shared context distribution among nodes with learnable personalized context distribution for each node .
Comparison of multidimensional swarm embedding techniques by potential fields Graph Embedding Based API Graph Search and Recommendation Locality Sensitive Discriminative Unsupervised Dimensionality Reduction Testing Convexity and Acyclicity, and New Constructions for Dense Graph Embeddings Graph Embedding based Tensor Analysis for Gait Recognition Hierarchical stochastic graphlet embedding for graph-based pattern recognition Predicting gene-disease associations via graph embedding and graph convolutional networks Graph Space Embedding Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks Gated Graph Sequence Neural Networks node2vec: Scalable Feature Learning for Networks Neural Message Passing for Quantum Chemistry Multi-Task Graph Autoencoders LINE: Large-scale Information Network Embedding Semi-Supervised Classification with Graph Convolutional Networks Question Answering with Subgraph Embeddings Hinge device and car trunk comprising same Lifting jack of safety gear Control valve means for a fluid system Secure and protective flexibly connection pole device for water guiding machine WYS*: A Verified Language Extension for Secure Multi-party Computations RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space Group Representation Theory for Knowledge Graph Embedding StepWatch accuracy during walking, running, and intermittent activities. Validity of Activity Monitor Step Detection Is Related to Movement Patterns. Graph Embedding with Personalized Context Distribution
318861df525c88e0360c80a474dd9cde53ade368 ac4e2470e8e780fbb04d2081a6985f4d4722a19d 42a8a2f281859890bbffba36ed0986112c52a853 89df3cc85976565a6828ad255b4e8f92de118fee f71ebb7e31901790255265c786da433ee2262ec5 2f4b1c4c1f9e2bf3939b11dcf1520a1d679f5d86 f6fd67f66d27860fa8e19e5ee1d30a9d5c6e75e3 448c69abd1abd0abe496090e0fe2368d8c589053 721bfedd0110e428df3be15b5fa099e9a556ec32 39397c8c6df280448f18b1de81bcdbcaec4e83a3 ba72ab7ff64faf7a7cd27e3809fee2ad8bacf302 94ce76b04b2277756bdf73855b84a1f0a5c17a47 c2660181b144b40d26884026943d66eda0f115d8 8f096071a09701012c9c279aee2a88143a295935 004749165374c58a096cb160d82b5d3c222d0130 63433216af326ae829530455c6b02c645480059b 88b556d810cb842bba586def62ae519164cf89a7 5bb3f0356f66ac94cb761795a5a658a1bcbe6add
["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science","Mathematics"] ["Medicine","Psychology"] ["Medicine","Computer Science"] ["Computer Science"]
2018 2019 2019 2019 2015 2020 2019 2019 2018 2016 2016 2017 2018 2015 2017 2014 2013 2009 2013 2007 2015 2019 2019 2017 2016 2020
BIBM IJCAI ArXiv ICLR KDD ICML ArXiv WWW '15 ICLR EMNLP ICLR (Poster) WWW (Companion Volume)
https://api.semanticscholar.org/318861df525c88e0360c80a474dd9cde53ade368 https://api.semanticscholar.org/ac4e2470e8e780fbb04d2081a6985f4d4722a19d https://api.semanticscholar.org/42a8a2f281859890bbffba36ed0986112c52a853 https://api.semanticscholar.org/89df3cc85976565a6828ad255b4e8f92de118fee https://api.semanticscholar.org/f71ebb7e31901790255265c786da433ee2262ec5 https://api.semanticscholar.org/2f4b1c4c1f9e2bf3939b11dcf1520a1d679f5d86 https://api.semanticscholar.org/f6fd67f66d27860fa8e19e5ee1d30a9d5c6e75e3 https://api.semanticscholar.org/448c69abd1abd0abe496090e0fe2368d8c589053 https://www.semanticscholar.org/paper/94eb48c1878efbe2ccff121bd600dd0fd8a75650 https://www.semanticscholar.org/paper/492f57ee9ceb61fb5a47ad7aebfec1121887a175 https://www.semanticscholar.org/paper/36ee2c8bd605afd48035d15fdc6b8c8842363376 https://www.semanticscholar.org/paper/e24cdf73b3e7e590c2fe5ecac9ae8aa983801367 https://www.semanticscholar.org/paper/c80803c31004ed869a54e7427398c239abc2d5e0 https://www.semanticscholar.org/paper/0834e74304b547c9354b6d7da6fa78ef47a48fa8 https://www.semanticscholar.org/paper/36eff562f65125511b5dfab68ce7f7a943c27478 https://www.semanticscholar.org/paper/33261d252218007147a71e40f8367ed152fa2fe0 https://api.semanticscholar.org/721bfedd0110e428df3be15b5fa099e9a556ec32 https://api.semanticscholar.org/39397c8c6df280448f18b1de81bcdbcaec4e83a3 https://api.semanticscholar.org/ba72ab7ff64faf7a7cd27e3809fee2ad8bacf302 https://api.semanticscholar.org/94ce76b04b2277756bdf73855b84a1f0a5c17a47 https://api.semanticscholar.org/c2660181b144b40d26884026943d66eda0f115d8 https://api.semanticscholar.org/8f096071a09701012c9c279aee2a88143a295935 https://api.semanticscholar.org/004749165374c58a096cb160d82b5d3c222d0130 https://api.semanticscholar.org/63433216af326ae829530455c6b02c645480059b https://api.semanticscholar.org/88b556d810cb842bba586def62ae519164cf89a7 https://api.semanticscholar.org/5bb3f0356f66ac94cb761795a5a658a1bcbe6add