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Recurrent Relational Networks
[article]
2018
arXiv
pre-print
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Using Pretty-CLEVR, we probe the limitations of multi-layer perceptrons, relational and recurrent relational networks. ...
We introduce the recurrent relational network, a general purpose module that operates on a graph representation of objects. ...
Recurrent Relational Networks We ground the discussion of a recurrent relational network in something familiar, solving a Sudoku puzzle. ...
arXiv:1711.08028v4
fatcat:tpgf65dhqrderfwfvthns72udq
Relational recurrent neural networks
[article]
2018
arXiv
pre-print
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Memory-based neural networks model temporal data by leveraging an ability to remember information for long periods. ...
., tasks involving relational reasoning. ...
reasoning in recurrent neural networks. ...
arXiv:1806.01822v2
fatcat:civthbeomzhixprxchbry33luq
Relation Classification via Recurrent Neural Network
[article]
2015
arXiv
pre-print
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In this paper, we propose a simple framework based on recurrent neural networks (RNN) and compare it with CNN-based model. ...
Deep learning has gained much success in sentence-level relation classification. ...
In this paper, we propose a simple framework based on recurrent neural networks (RNN) and compare it with CNN-based model. ...
arXiv:1508.01006v2
fatcat:gcaxzbd35neatg6twznxxta3zq
A Recurrent Graph Neural Network for Multi-Relational Data
[article]
2019
arXiv
pre-print
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In this paper, we introduce a graph recurrent neural network (GRNN) for scalable semi-supervised learning from multi-relational data. ...
Key aspects of the novel GRNN architecture are the use of multi-relational graphs, the dynamic adaptation to the different relations via learnable weights, and the consideration of graph-based regularizers ...
Albeit their ubiquitous presence, development of SSL methods that account for multi-relational networks is only in its infancy, see, e.g., [1, 3] . Related work. ...
arXiv:1811.02061v3
fatcat:lcjdu4s7jffkjjtwzh6odqprli
Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks
[article]
2017
arXiv
pre-print
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Our goal is to combine the rich multistep inference of symbolic logical reasoning with the generalization capabilities of neural networks. ...
(3) we learn to share strength in a single RNN that represents logical composition across all relations. ...
Reasoning is performed nonatomically about conjunctions of relations in an arbitrary length path by composing them with a recurrent neural network (RNN). ...
arXiv:1607.01426v3
fatcat:2ty3lxyldbeadajgbx6fuuhqle
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations
[article]
2020
arXiv
pre-print
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As a solution, we design a multi-task learning model which we refer to as recurrent interaction network which allows the learning of interactions dynamically, to effectively model task-specific features ...
networks for prediction. ...
Model In this section, we describe the recurrent interaction network (RIN) for extracting relational facts in text. ...
arXiv:2005.00162v2
fatcat:6ihbmzlswjemtbvdbiasx2t6ne
Recurrent Relational Memory Network for Unsupervised Image Captioning
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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In this paper, we propose a novel memory-based network rather than GAN, named Recurrent Relational Memory Network (R2M). ...
memories, correlating the relational reasoning between common visual concepts and the generated words for long periods. ...
Orthogonal to above GAN-based models, in this paper, we propose a novel memory-based solution, named Recurrent Relational Memory Network (R 2 M ). ...
doi:10.24963/ijcai.2020/128
dblp:conf/ijcai/GuoWSW20
fatcat:ttv3qb2kw5fyjatm3fknc25uk4
Recurrent Relational Memory Network for Unsupervised Image Captioning
[article]
2020
arXiv
pre-print
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In this paper, we propose a novel memory-based network rather than GAN, named Recurrent Relational Memory Network (R^2M). ...
memories, correlating the relational reasoning between common visual concepts and the generated words for long periods. ...
Figure 2 : 2 An overview of R 2 M (Recurrent Relational Memory network)
Figure 3 : 3 Memory mechanism in R 2 M.Decoder. ...
arXiv:2006.13611v1
fatcat:w5uwfq6tknevzin2zoxj5gpgy4
Bidirectional Recurrent Convolutional Neural Network for Relation Classification
2016
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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We further explore how to make full use of the dependency relations information in the SDP, by combining convolutional neural networks and twochannel recurrent neural networks with long short term memory ...
Some state-of-the-art systems concentrate on modeling the shortest dependency path (SDP) between two entities leveraging convolutional or recurrent neural networks. ...
The NN research for relation classification has centered around two main network architectures: convolutional neural networks and recursive/recurrent neural networks. ...
doi:10.18653/v1/p16-1072
dblp:conf/acl/CaiZW16
fatcat:cflgpg7zefflnaqrdov5qkxbhq
Relational Recurrent Neural Networks For Vehicle Trajectory Prediction
2019
2019 IEEE Intelligent Transportation Systems Conference (ITSC)
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Knowing the performance of Long Short Term Memories (LSTMs) in sequence modeling and the power of attention mechanism to capture long range dependencies, we bring relational recurrent neural networks ( ...
The originality of this network is that it combines the advantages of the LSTM blocks in representing the temporal evolution of trajectories and the attention mechanism to model the relative interactions ...
The proposed architecture is based on Relational Recurrent Neural Networks (RRNNs) [13] encoder decoder. ...
doi:10.1109/itsc.2019.8916887
dblp:conf/itsc/MessaoudYVN19
fatcat:t37h24wjsjg4njvv63lap75gcm
Structure Regularized Bidirectional Recurrent Convolutional Neural Network for Relation Classification
[article]
2017
arXiv
pre-print
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In this paper, we present a novel model, Structure Regularized Bidirectional Recurrent Convolutional Neural Network(SR-BRCNN), to classify the relation of two entities in a sentence, and the new dataset ...
Some state-of-the-art systems concentrate on modeling the shortest dependency path (SDP) between two entities leveraging convolutional or recurrent neural networks. ...
A number of convolutional neural network (CNN), recurrent neural network (RNN), and other neural architectures have been proposed for relation classification. ...
arXiv:1711.02509v1
fatcat:wnehk2vemnd5vgo2h7gdwe23eu
A Latent Variable Recurrent Neural Network for Discourse Relation Language Models
[article]
2016
arXiv
pre-print
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This paper presents a novel latent variable recurrent neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences. ...
A recurrent neural network generates individual words, thus reaping the benefits of discriminatively-trained vector representations. ...
Discourse relations z t are treated as latent variables, which are linked with a recurrent neural network over words in a latent variable recurrent neural network (Chung et al., 2015) . ...
arXiv:1603.01913v2
fatcat:4bsf55cb6rfxrnivsqwynenu3u
Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling
[article]
2020
arXiv
pre-print
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We apply the Recurrent-DBN to dynamic relational data problems. ...
In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable ...
In this work, we propose a Recurrent Dirichlet Belief Network (Recurrent-DBN) to explore the complex latent structures in dynamic relational data. ...
arXiv:2002.10235v2
fatcat:puruxwpcbffr5cnocl7xbhyotq
Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition
[article]
2016
arXiv
pre-print
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Instead of using a traditional inference method, we use a sequential inference modeled by a recurrent neural network. ...
Rich semantic relations are important in a variety of visual recognition problems. ...
This network structure is a recurrent neural network (RNN). ...
arXiv:1511.04196v2
fatcat:xlhcepz44jfuxmwo6rc6tf3ckq
Character-based recurrent neural networks for morphological relational reasoning
2017
Proceedings of the First Workshop on Subword and Character Level Models in NLP
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To address the task of predicting a word form given a demo relation (a pair of word forms) and a query word, we devise a character-based recurrent neural network architecture using three separate encoders ...
We present a model for predicting word forms based on morphological relational reasoning with analogies. ...
Recurrent neural networks A recurrent neural network (RNN) is an artificial neural network that can model a sequence of arbitrary length. ...
doi:10.18653/v1/w17-4108
dblp:conf/emnlp/MogrenJ17
fatcat:227sbxy53ncgrpxcbek3wdjm4u
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