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Such structured sequences can represent series of frames in", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 141, + 318, + 470, + 330 + ], + "spans": [ + { + "bbox": [ + 141, + 318, + 470, + 330 + ], + "score": 1.0, + "content": "videos, spatio-temporal measurements on a network of sensors, or random walks", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 141, + 329, + 470, + 342 + ], + "spans": [ + { + "bbox": [ + 141, + 329, + 470, + 342 + ], + "score": 1.0, + "content": "on a vocabulary graph for natural language modeling. The proposed model com-", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 141, + 340, + 469, + 352 + ], + "spans": [ + { + "bbox": [ + 141, + 340, + 469, + 352 + ], + "score": 1.0, + "content": "bines convolutional neural networks (CNN) on graphs to identify spatial structures", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 141, + 351, + 470, + 363 + ], + "spans": [ + { + "bbox": [ + 141, + 351, + 470, + 363 + ], + "score": 1.0, + "content": "and RNN to find dynamic patterns. We study two possible architectures of GCRN,", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 141, + 362, + 470, + 375 + ], + "spans": [ + { + "bbox": [ + 141, + 362, + 470, + 375 + ], + "score": 1.0, + "content": "and apply the models to two practical problems: predicting moving MNIST data,", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 142, + 373, + 469, + 385 + ], + "spans": [ + { + "bbox": [ + 142, + 373, + 469, + 385 + ], + "score": 1.0, + "content": "and modeling natural language with the Penn Treebank dataset. Experiments show", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 141, + 384, + 470, + 396 + ], + "spans": [ + { + "bbox": [ + 141, + 384, + 470, + 396 + ], + "score": 1.0, + "content": "that exploiting simultaneously graph spatial and dynamic information about data", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 141, + 395, + 333, + 407 + ], + "spans": [ + { + "bbox": [ + 141, + 395, + 333, + 407 + ], + "score": 1.0, + "content": "can improve both precision and learning speed.", + "type": "text" + } + ], + "index": 26 + } + ], + "index": 20.5, + "bbox_fs": [ + 140, + 272, + 470, + 407 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 430, + 206, + 443 + ], + "lines": [ + { + "bbox": [ + 105, + 429, + 208, + 446 + ], + "spans": [ + { + "bbox": [ + 105, + 429, + 208, + 446 + ], + "score": 1.0, + "content": "1 INTRODUCTION", + "type": "text" + } + ], + "index": 27 + } + ], + "index": 27 + }, + { + "type": "text", + "bbox": [ + 107, + 456, + 505, + 545 + ], + "lines": [ + { + "bbox": [ + 105, + 456, + 506, + 470 + ], + "spans": [ + { + "bbox": [ + 105, + 456, + 506, + 470 + ], + "score": 1.0, + "content": "Many real-world data can be cast as structured sequences, with spatio-temporal sequences being a", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 467, + 506, + 480 + ], + "spans": [ + { + "bbox": [ + 105, + 467, + 506, + 480 + ], + "score": 1.0, + "content": "special case. A well-studied example of spatio-temporal data are videos, where succeeding frames", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 478, + 505, + 491 + ], + "spans": [ + { + "bbox": [ + 105, + 478, + 505, + 491 + ], + "score": 1.0, + "content": "share temporal and spatial structures. Many works, such as Donahue et al. (2015); Karpathy & Fei-", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 490, + 505, + 502 + ], + "spans": [ + { + "bbox": [ + 105, + 490, + 505, + 502 + ], + "score": 1.0, + "content": "Fei (2015); Vinyals et al. (2015), leveraged a combination of CNN and RNN to exploit such spatial", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 501, + 505, + 513 + ], + "spans": [ + { + "bbox": [ + 106, + 501, + 505, + 513 + ], + "score": 1.0, + "content": "and temporal regularities. 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(2015) proposed a model for tree-structured topologies, where each LSTM has", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 615, + 506, + 628 + ], + "spans": [ + { + "bbox": [ + 105, + 615, + 506, + 628 + ], + "score": 1.0, + "content": "access to the states of its children. They obtained state-of-the-art results on semantic relatedness and", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 626, + 506, + 640 + ], + "spans": [ + { + "bbox": [ + 105, + 626, + 506, + 640 + ], + "score": 1.0, + "content": "sentiment classification. Liang et al. (2016) followed up and proposed a variant on graphs. 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(2016) developed a method to cast a spatio-temporal graph as a rich RNN mixture which", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 106, + 82, + 504, + 95 + ], + "spans": [ + { + "bbox": [ + 106, + 82, + 504, + 95 + ], + "score": 1.0, + "content": "essentially associates a RNN to each node and edge. 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ArchitectureStructureFilter sizeParametersRuntimeTest(w/o Rot)Test(Rot)
FC-LSTMN/AN/A142,667,776N/A4832=
LSTM+CNNN/A5×513,524,4962.1038514339
LSTM+CNNN/A9×943,802,1286.1039034208
LSTM+GCNNknn=8K=31,629,7120.8238664367
LSTM+GCNNknn=8K=52,711,0561.2434953932
LSTM+GCNNknn=8K=73,792,4001.6134003803
LSTM+GCNNknn=8K=94,873,7442.1533953814
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(2015), structure-aware LSTM cells can be stacked and used as", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 381, + 505, + 393 + ], + "spans": [ + { + "bbox": [ + 105, + 381, + 505, + 393 + ], + "score": 1.0, + "content": "sequence-to-sequence models using an architecture composed of an encoder, which processes the", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 392, + 505, + 405 + ], + "spans": [ + { + "bbox": [ + 106, + 392, + 505, + 405 + ], + "score": 1.0, + "content": "input sequence, and a decoder, which generates an output sequence. A standard practice for machine", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 106, + 403, + 369, + 415 + ], + "spans": [ + { + "bbox": [ + 106, + 403, + 369, + 415 + ], + "score": 1.0, + "content": "translation using RNNs (Cho et al., 2014; Sutskever et al., 2014).", + "type": "text" + } + ], + "index": 16 + } + ], + "index": 14.5 + }, + { + "type": "title", + "bbox": [ + 108, + 430, + 200, + 443 + ], + "lines": [ + { + "bbox": [ + 105, + 429, + 201, + 445 + ], + "spans": [ + { + "bbox": [ + 105, + 429, + 201, + 445 + ], + "score": 1.0, + "content": "5 EXPERIMENTS", + "type": "text" + } + ], + "index": 17 + } + ], + "index": 17 + }, + { + "type": "title", + "bbox": [ + 108, + 455, + 405, + 466 + ], + "lines": [ + { + "bbox": [ + 105, + 454, + 407, + 468 + ], + "spans": [ + { + "bbox": [ + 105, + 454, + 407, + 468 + ], + "score": 1.0, + "content": "5.1 SPATIO-TEMPORAL SEQUENCE MODELING ON MOVING-MNIST", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 18 + }, + { + "type": "text", + "bbox": [ + 106, + 476, + 505, + 608 + ], + "lines": [ + { + "bbox": [ + 105, + 475, + 505, + 489 + ], + "spans": [ + { + "bbox": [ + 105, + 475, + 505, + 489 + ], + "score": 1.0, + "content": "For this synthetic experiment, we use the moving-MNIST dataset generated by Shi et al. 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Following their experimental setup, all", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 104, + 509, + 505, + 523 + ], + "spans": [ + { + "bbox": [ + 104, + 509, + 505, + 523 + ], + "score": 1.0, + "content": "models are trained by minimizing the binary cross-entropy loss using back-propagation through", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 104, + 519, + 506, + 533 + ], + "spans": [ + { + "bbox": [ + 104, + 519, + 317, + 533 + ], + "score": 1.0, + "content": "time (BPTT) and RMSProp with a learning rate of", + "type": "text" + }, + { + "bbox": [ + 317, + 519, + 339, + 530 + ], + "score": 0.9, + "content": "1 0 ^ { - 3 }", + "type": "inline_equation" + }, + { + "bbox": [ + 339, + 519, + 506, + 533 + ], + "score": 1.0, + "content": "and a decay rate of 0.9. 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(2015), structure-aware LSTM cells can be stacked and used as", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 381, + 505, + 393 + ], + "spans": [ + { + "bbox": [ + 105, + 381, + 505, + 393 + ], + "score": 1.0, + "content": "sequence-to-sequence models using an architecture composed of an encoder, which processes the", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 392, + 505, + 405 + ], + "spans": [ + { + "bbox": [ + 106, + 392, + 505, + 405 + ], + "score": 1.0, + "content": "input sequence, and a decoder, which generates an output sequence. A standard practice for machine", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 106, + 403, + 369, + 415 + ], + "spans": [ + { + "bbox": [ + 106, + 403, + 369, + 415 + ], + "score": 1.0, + "content": "translation using RNNs (Cho et al., 2014; Sutskever et al., 2014).", + "type": "text" + } + ], + "index": 16 + } + ], + "index": 14.5, + "bbox_fs": [ + 105, + 370, + 506, + 415 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 430, + 200, + 443 + ], + "lines": [ + { + "bbox": [ + 105, + 429, + 201, + 445 + ], + "spans": [ + { + "bbox": [ + 105, + 429, + 201, + 445 + ], + "score": 1.0, + "content": "5 EXPERIMENTS", + "type": "text" + } + ], + "index": 17 + } + ], + "index": 17 + }, + { + "type": "title", + "bbox": [ + 108, + 455, + 405, + 466 + ], + "lines": [ + { + "bbox": [ + 105, + 454, + 407, + 468 + ], + "spans": [ + { + "bbox": [ + 105, + 454, + 407, + 468 + ], + "score": 1.0, + "content": "5.1 SPATIO-TEMPORAL SEQUENCE MODELING ON MOVING-MNIST", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 18 + }, + { + "type": "text", + "bbox": [ + 106, + 476, + 505, + 608 + ], + "lines": [ + { + "bbox": [ + 105, + 475, + 505, + 489 + ], + "spans": [ + { + "bbox": [ + 105, + 475, + 505, + 489 + ], + "score": 1.0, + "content": "For this synthetic experiment, we use the moving-MNIST dataset generated by Shi et al. (2015).", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 487, + 505, + 499 + ], + "spans": [ + { + "bbox": [ + 106, + 487, + 505, + 499 + ], + "score": 1.0, + "content": "All sequences are 20 frames long (10 frames as input and 10 frames for prediction) and contain", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 497, + 506, + 511 + ], + "spans": [ + { + "bbox": [ + 105, + 497, + 277, + 511 + ], + "score": 1.0, + "content": "two handwritten digits bouncing inside a", + "type": "text" + }, + { + "bbox": [ + 277, + 498, + 312, + 509 + ], + "score": 0.9, + "content": "6 4 \\times 6 4", + "type": "inline_equation" + }, + { + "bbox": [ + 313, + 497, + 506, + 511 + ], + "score": 1.0, + "content": "patch. Following their experimental setup, all", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 104, + 509, + 505, + 523 + ], + "spans": [ + { + "bbox": [ + 104, + 509, + 505, + 523 + ], + "score": 1.0, + "content": "models are trained by minimizing the binary cross-entropy loss using back-propagation through", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 104, + 519, + 506, + 533 + ], + "spans": [ + { + "bbox": [ + 104, + 519, + 317, + 533 + ], + "score": 1.0, + "content": "time (BPTT) and RMSProp with a learning rate of", + "type": "text" + }, + { + "bbox": [ + 317, + 519, + 339, + 530 + ], + "score": 0.9, + "content": "1 0 ^ { - 3 }", + "type": "inline_equation" + }, + { + "bbox": [ + 339, + 519, + 506, + 533 + ], + "score": 1.0, + "content": "and a decay rate of 0.9. We choose the", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 531, + 506, + 544 + ], + "spans": [ + { + "bbox": [ + 105, + 531, + 506, + 544 + ], + "score": 1.0, + "content": "best model with early-stopping on validation set. All implementations are based on their Theano", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 541, + 505, + 555 + ], + "spans": [ + { + "bbox": [ + 105, + 541, + 271, + 555 + ], + "score": 1.0, + "content": "code and dataset.3 The adjacency matrix", + "type": "text" + }, + { + "bbox": [ + 272, + 542, + 280, + 552 + ], + "score": 0.67, + "content": "A", + "type": "inline_equation" + }, + { + "bbox": [ + 281, + 541, + 357, + 555 + ], + "score": 1.0, + "content": "is constructed as a", + "type": "text" + }, + { + "bbox": [ + 357, + 542, + 364, + 552 + ], + "score": 0.28, + "content": "\\mathbf { k }", + "type": "inline_equation" + }, + { + "bbox": [ + 364, + 541, + 505, + 555 + ], + "score": 1.0, + "content": "-nearest-neighbor (knn) graph with", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 106, + 553, + 505, + 565 + ], + "spans": [ + { + "bbox": [ + 106, + 553, + 505, + 565 + ], + "score": 1.0, + "content": "Euclidean distance and Gaussian kernel between pixel locations. 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(2015) defined in (6), all GCRN experiments are conducted with Model 2 defined in (9), which", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 104, + 574, + 505, + 587 + ], + "spans": [ + { + "bbox": [ + 104, + 574, + 304, + 587 + ], + "score": 1.0, + "content": "is the same architecture with the 2D convolution", + "type": "text" + }, + { + "bbox": [ + 304, + 577, + 312, + 585 + ], + "score": 0.76, + "content": "^ *", + "type": "inline_equation" + }, + { + "bbox": [ + 312, + 574, + 444, + 587 + ], + "score": 1.0, + "content": "replaced by a graph convolution", + "type": "text" + }, + { + "bbox": [ + 444, + 576, + 457, + 587 + ], + "score": 0.85, + "content": "^ { \\ast _ { \\mathcal { G } } }", + "type": "inline_equation" + }, + { + "bbox": [ + 457, + 574, + 505, + 587 + ], + "score": 1.0, + "content": ". 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ArchitectureRepresentationParametersTrain PerplexityTest Perplexity
Zaremba et al. (2014) code6embedding681,80036.96117.29
Zaremba et al. (2014) code6one-hot34,011,60053.89118.82
LSTMembedding681,80048.38120.90
LSTMone-hot34,011,60054.41120.16
LSTM, dropoutone-hot34,011,600145.59112.98
GCRN-M1one-hot42,011,60218.49177.14
GCRN-M1, dropoutone-hot42,011,602114.2998.67
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This", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 141, + 656, + 505, + 668 + ], + "spans": [ + { + "bbox": [ + 141, + 656, + 505, + 668 + ], + "score": 1.0, + "content": "bad performance may be the result of the large increase of dimensionality in Model 2, as", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 141, + 667, + 505, + 680 + ], + "spans": [ + { + "bbox": [ + 141, + 667, + 505, + 680 + ], + "score": 1.0, + "content": "the dimension of the hidden and cell states changes from 200 to 10,000, the size of the", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 142, + 679, + 505, + 690 + ], + "spans": [ + { + "bbox": [ + 142, + 679, + 505, + 690 + ], + "score": 1.0, + "content": "vocabulary. 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ArchitectureRepresentationParametersTrain PerplexityTest Perplexity
Zaremba et al. (2014) code6embedding681,80036.96117.29
Zaremba et al. (2014) code6one-hot34,011,60053.89118.82
LSTMembedding681,80048.38120.90
LSTMone-hot34,011,60054.41120.16
LSTM, dropoutone-hot34,011,600145.59112.98
GCRN-M1one-hot42,011,60218.49177.14
GCRN-M1, dropoutone-hot42,011,602114.2998.67
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This choice allows us", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 455, + 264, + 467 + ], + "spans": [ + { + "bbox": [ + 106, + 455, + 264, + 467 + ], + "score": 1.0, + "content": "to use the graph structure of the words.", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 13.5, + "bbox_fs": [ + 106, + 443, + 505, + 467 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 472, + 505, + 505 + ], + "lines": [ + { + "bbox": [ + 105, + 471, + 505, + 485 + ], + "spans": [ + { + "bbox": [ + 105, + 471, + 505, + 485 + ], + "score": 1.0, + "content": "Table 2 reports the final train and test perplexity values for each investigated model and Figure 5", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 106, + 483, + 505, + 495 + ], + "spans": [ + { + "bbox": [ + 106, + 483, + 505, + 495 + ], + "score": 1.0, + "content": "plots the perplexity value vs. the number of epochs for the train and test sets with and without", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 106, + 494, + 322, + 506 + ], + "spans": [ + { + "bbox": [ + 106, + 494, + 322, + 506 + ], + "score": 1.0, + "content": "dropout regularization. Numerical experiments show:", + "type": "text" + } + ], + "index": 17 + } + ], + "index": 16, + "bbox_fs": [ + 105, + 471, + 505, + 506 + ] + }, + { + "type": "list", + "bbox": [ + 128, + 514, + 505, + 701 + ], + "lines": [ + { + "bbox": [ + 130, + 514, + 504, + 527 + ], + "spans": [ + { + "bbox": [ + 130, + 514, + 504, + 527 + ], + "score": 1.0, + "content": "1. Given the same experimental conditions in terms of architecture and no dropout regulariza-", + "type": "text" + } + ], + "index": 18, + "is_list_start_line": true + }, + { + "bbox": [ + 141, + 525, + 505, + 537 + ], + "spans": [ + { + "bbox": [ + 141, + 525, + 505, + 537 + ], + "score": 1.0, + "content": "tion, the standalone model of LSTM is more accurate than LSTM using the spatial graph", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 141, + 534, + 506, + 549 + ], + "spans": [ + { + "bbox": [ + 141, + 534, + 506, + 549 + ], + "score": 1.0, + "content": "information (120.16 vs. 177.14), extracted by graph CNN with the GCRN architecture of", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 142, + 547, + 214, + 559 + ], + "spans": [ + { + "bbox": [ + 142, + 547, + 214, + 559 + ], + "score": 1.0, + "content": "Model 1, Eq. 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The use of spatial graph information found by graph CNN speeds up the learning process,", + "type": "text" + } + ], + "index": 24, + "is_list_start_line": true + }, + { + "bbox": [ + 141, + 597, + 506, + 612 + ], + "spans": [ + { + "bbox": [ + 141, + 597, + 506, + 612 + ], + "score": 1.0, + "content": "and overfits the training dataset in the absence of dropout regularization. The graph struc-", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 141, + 608, + 506, + 622 + ], + "spans": [ + { + "bbox": [ + 141, + 608, + 506, + 622 + ], + "score": 1.0, + "content": "ture likely acts a constraint on the learning system that is forced to move in the space of", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 141, + 619, + 209, + 633 + ], + "spans": [ + { + "bbox": [ + 141, + 619, + 209, + 633 + ], + "score": 1.0, + "content": "language topics.", + "type": "text" + } + ], + "index": 27, + "is_list_end_line": true + }, + { + "bbox": [ + 129, + 635, + 505, + 647 + ], + "spans": [ + { + "bbox": [ + 129, + 635, + 505, + 647 + ], + "score": 1.0, + "content": "4. We performed the same experiments with LSTM and Model 2 defined in (9). Model 1", + "type": "text" + } + ], + "index": 28, + "is_list_start_line": true + }, + { + "bbox": [ + 141, + 645, + 506, + 658 + ], + "spans": [ + { + "bbox": [ + 141, + 645, + 506, + 658 + ], + "score": 1.0, + "content": "significantly outperformed Model 2, and Model 2 did worse than standalone LSTM. This", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 141, + 656, + 505, + 668 + ], + "spans": [ + { + "bbox": [ + 141, + 656, + 505, + 668 + ], + "score": 1.0, + "content": "bad performance may be the result of the large increase of dimensionality in Model 2, as", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 141, + 667, + 505, + 680 + ], + "spans": [ + { + "bbox": [ + 141, + 667, + 505, + 680 + ], + "score": 1.0, + "content": "the dimension of the hidden and cell states changes from 200 to 10,000, the size of the", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 142, + 679, + 505, + 690 + ], + "spans": [ + { + "bbox": [ + 142, + 679, + 505, + 690 + ], + "score": 1.0, + "content": "vocabulary. 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Right: 3D visualization of words’ structure.", + "type": "text" + } + ], + "index": 4 + } + ], + "index": 4 + } + ], + "index": 2.5 + }, + { + "type": "title", + "bbox": [ + 107, + 257, + 303, + 270 + ], + "lines": [ + { + "bbox": [ + 105, + 256, + 304, + 272 + ], + "spans": [ + { + "bbox": [ + 105, + 256, + 304, + 272 + ], + "score": 1.0, + "content": "6 CONCLUSION AND FUTURE WORK", + "type": "text" + } + ], + "index": 5 + } + ], + "index": 5 + }, + { + "type": "text", + "bbox": [ + 107, + 284, + 505, + 427 + ], + "lines": [ + { + "bbox": [ + 105, + 284, + 505, + 297 + ], + "spans": [ + { + "bbox": [ + 105, + 284, + 505, + 297 + ], + "score": 1.0, + "content": "This work aims at learning spatio-temporal structures from graph-structured and time-varying data.", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 295, + 505, + 307 + ], + "spans": [ + { + "bbox": [ + 106, + 295, + 505, + 307 + ], + "score": 1.0, + "content": "In this context, the main challenge is to identify the best possible architecture that combines simul-", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 307, + 505, + 318 + ], + "spans": [ + { + "bbox": [ + 105, + 307, + 505, + 318 + ], + "score": 1.0, + "content": "taneously recurrent neural networks like vanilla RNN, LSTM or GRU with convolutional neural", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 317, + 506, + 330 + ], + "spans": [ + { + "bbox": [ + 105, + 317, + 506, + 330 + ], + "score": 1.0, + "content": "networks for graph-structured data. We have investigated here two architectures, one using a stack", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 327, + 505, + 341 + ], + "spans": [ + { + "bbox": [ + 105, + 327, + 505, + 341 + ], + "score": 1.0, + "content": "of CNN and RNN (Model 1), and one using convLSTM that considers convolutions instead of fully", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 338, + 505, + 352 + ], + "spans": [ + { + "bbox": [ + 105, + 338, + 505, + 352 + ], + "score": 1.0, + "content": "connected operations in the RNN definition (Model 2). We have then considered two applications:", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 350, + 505, + 363 + ], + "spans": [ + { + "bbox": [ + 105, + 350, + 505, + 363 + ], + "score": 1.0, + "content": "video prediction and natural language modeling. Model 2 has shown good performances in the", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 360, + 506, + 374 + ], + "spans": [ + { + "bbox": [ + 105, + 360, + 506, + 374 + ], + "score": 1.0, + "content": "case of video prediction, by improving the results of Shi et al. (2015). Model 1 has also provided", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 372, + 505, + 384 + ], + "spans": [ + { + "bbox": [ + 105, + 372, + 505, + 384 + ], + "score": 1.0, + "content": "promising performances in the case of language modeling, particularly in terms of learning speed.", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 383, + 505, + 396 + ], + "spans": [ + { + "bbox": [ + 105, + 383, + 505, + 396 + ], + "score": 1.0, + "content": "It has been shown that (i) isotropic filters, maybe surprisingly, can outperform classical 2D filters", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 394, + 505, + 407 + ], + "spans": [ + { + "bbox": [ + 105, + 394, + 505, + 407 + ], + "score": 1.0, + "content": "on images while requiring much less parameters, and (ii) that graphs coupled with graph CNN and", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 104, + 403, + 506, + 419 + ], + "spans": [ + { + "bbox": [ + 104, + 403, + 506, + 419 + ], + "score": 1.0, + "content": "RNN are a versatile way of introducing and exploiting side-information, e.g. the semantic of words,", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 416, + 221, + 428 + ], + "spans": [ + { + "bbox": [ + 105, + 416, + 221, + 428 + ], + "score": 1.0, + "content": "by structuring a data matrix.", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 12 + }, + { + "type": "text", + "bbox": [ + 107, + 433, + 505, + 510 + ], + "lines": [ + { + "bbox": [ + 105, + 433, + 505, + 445 + ], + "spans": [ + { + "bbox": [ + 105, + 433, + 505, + 445 + ], + "score": 1.0, + "content": "Future work will investigate applications to data naturally structured as dynamic graph signals, for", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 445, + 505, + 456 + ], + "spans": [ + { + "bbox": [ + 106, + 445, + 505, + 456 + ], + "score": 1.0, + "content": "instance fMRI and sensor networks. The graph CNN model we have used is rotationally-invariant", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 455, + 505, + 467 + ], + "spans": [ + { + "bbox": [ + 105, + 455, + 505, + 467 + ], + "score": 1.0, + "content": "and such spatial property seems quite attractive in real situations where motion is beyond translation.", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 106, + 465, + 505, + 479 + ], + "spans": [ + { + "bbox": [ + 106, + 465, + 505, + 479 + ], + "score": 1.0, + "content": "We will also investigate how to benefit of the fast learning property of our system to speed up", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 476, + 506, + 490 + ], + "spans": [ + { + "bbox": [ + 105, + 476, + 506, + 490 + ], + "score": 1.0, + "content": "language modeling models. Eventually, it will be interesting to analyze the underlying dynamical", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 487, + 505, + 501 + ], + "spans": [ + { + "bbox": [ + 105, + 487, + 505, + 501 + ], + "score": 1.0, + "content": "property of generic RNN architectures in the case of graphs. Graph structures may introduce stability", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 499, + 407, + 511 + ], + "spans": [ + { + "bbox": [ + 105, + 499, + 407, + 511 + ], + "score": 1.0, + "content": "to RNN systems, and prevent them to express unstable dynamic behaviors.", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 22 + }, + { + "type": "title", + "bbox": [ + 108, + 529, + 212, + 541 + ], + "lines": [ + { + "bbox": [ + 107, + 529, + 214, + 543 + ], + "spans": [ + { + "bbox": [ + 107, + 529, + 214, + 543 + ], + "score": 1.0, + "content": "ACKNOWLEDGMENT", + "type": "text" + } + ], + "index": 26 + } + ], + "index": 26 + }, + { + "type": "text", + "bbox": [ + 107, + 555, + 504, + 577 + ], + "lines": [ + { + "bbox": [ + 106, + 555, + 505, + 568 + ], + "spans": [ + { + "bbox": [ + 106, + 555, + 505, + 568 + ], + "score": 1.0, + "content": "This research was supported in part by the European Union’s H2020 Framework Programme", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 565, + 479, + 579 + ], + "spans": [ + { + "bbox": [ + 105, + 565, + 479, + 579 + ], + "score": 1.0, + "content": "(H2020-MSCA-ITN-2014) under grant No. 642685 MacSeNet, and Nvidia equipment grant.", + "type": "text" + } + ], + "index": 28 + } + ], + "index": 27.5 + }, + { + "type": "title", + "bbox": [ + 108, + 595, + 175, + 607 + ], + "lines": [ + { + "bbox": [ + 106, + 595, + 176, + 609 + ], + "spans": [ + { + "bbox": [ + 106, + 595, + 176, + 609 + ], + "score": 1.0, + "content": "REFERENCES", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 29 + }, + { + "type": "text", + "bbox": [ + 108, + 614, + 505, + 635 + ], + "lines": [ + { + "bbox": [ + 106, + 614, + 505, + 625 + ], + "spans": [ + { + "bbox": [ + 106, + 614, + 505, + 625 + ], + "score": 1.0, + "content": "Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 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