Fetching the paper…
Reading the bibliography…
We introduce the framework of continuous--depth graph neural networks (GNNs).
Beitrag zur naherungsweisen integration totaler differentialgleichungen
W. Kutta · 1901
Earlier work this paper cites.
The mathematical theory of optimal processes
L. S. Pontryagin, E. Mishchenko, V. Boltyanskii, and R. Gamkrelidze · 1962
Earlier work this paper cites.
A family of embedded runge-kutta formulae
J. R. Dormand and P. J. Prince · 1980
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
An introduction to hybrid dynamical systems , volume 251
A. J. Van Der Schaft and J. M. Schumacher · 2000
Earlier work this paper cites.
A time-varying complex dynamical network model and its controlled synchronization criteria
J. Lu and G. Chen · 2005
Earlier work this paper cites.
Collective classification in network data
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad · 2008
Earlier work this paper cites.
Hybrid dynamical systems
R. Goebel, R. G. Sanfelice, and A. R. Teel · 2009
Earlier work this paper cites.
Minimal repetition dynamic checkpointing algorithm for unsteady adjoint calculation
Q. Wang, P. Moin, and G. Iaccarino · 2009
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2013
Earlier work this paper cites.
Discrete signal processing on graphs
A. Sandryhaila and J. M. Moura · 2013
Earlier work this paper cites.
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst · 2013
Earlier work this paper cites.
Distributed control of networked dynamical systems: Static feedback, integral action and consensus
M. Andreasson, D. V. Dimarogonas, H. Sandberg, and K. H. Johansson · 2014
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Diffusion-convolutional neural networks
J. Atwood and D. Towsley · 2016
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
Earlier work this paper cites.
Fractalnet: Ultra-deep neural networks without residuals
G. Larsson, M. Maire, and G. Shakhnarovich · 2016
Cited alongside, same era.
Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2016
Cited alongside, same era.
Gram: graph-based attention model for healthcare representation learning
E. Choi, M. T. Bahadori, L. Song, W. F. Stewart, and J. Sun · 2017
Cited alongside, same era.
Stable architectures for deep neural networks
E. Haber and L. Ruthotto · 2017
Cited alongside, same era.
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Y. Li, R. Yu, C. Shahabi, and Y. Liu · 2017
Cited alongside, same era.
Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
B. Yu, H. Yin, and Z. Zhu · 2018
Later among the works it cites.
Deep learning for predicting dynamic uncertain opinions in network data
X. Zhao, F. Chen, and J.-H. Cho · 2018
Later among the works it cites.
Dual graph convolutional networks for graph-based semi-supervised classification
C. Zhuang and Q. Ma · 2018
Later among the works it cites.
Continuous graph flow, 2019
Z. Deng, M. Nawhal, L. Meng, and G. Mori · 2019
Closest in time.
E. Dupont, A. Doucet, and Y. W. Teh · 2019
Closest in time.
Fast and deep graph neural networks, 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Lu, A. Zhong, Q. Li, and B. Dong · 2017
Cited alongside, same era.
Geometric deep learning on graphs and manifolds using mixture model cnns
F. Monti, D. Boscaini, J. Masci, E. Rodola, J. Svoboda, and M. M. Bronstein · 2017
Cited alongside, same era.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2017
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al · 2018
Cited alongside, same era.
Recurrent neural networks for multivariate time series with missing values
Z. Che, S. Purushotham, K. Cho, D. Sontag, and Y. Liu · 2018
Cited alongside, same era.
Neural ordinary differential equations
T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud · 2018
Cited alongside, same era.
C. Gallicchio and A. Micheli · 2019
Closest in time.
Exact combinatorial optimization with graph convolutional neural networks
M. Gasse, D. Chételat, N. Ferroni, L. Charlin, and A. Lodi · 2019
Closest in time.
Anode: Unconditionally accurate memory-efficient gradients for neural odes
A. Gholami, K. Keutzer, and G. Biros · 2019
Closest in time.
Graph normalizing flows
J. Liu, A. Kumar, J. Ba, J. Kiros, and K. Swersky · 2019
Closest in time.
Graph neural networks exponentially lose expressive power for node classification, 2019
K. Oono and T. Suzuki · 2019
Closest in time.
Recurrent neural networks for time series forecasting
G. Petneházi · 2019
Closest in time.
Accelerating neural odes with spectral elements
A. Quaglino, M. Gallieri, J. Masci, and J. Koutník · 2019
Closest in time.
Latent odes for irregularly-sampled time series
Y. Rubanova, R. T. Chen, and D. Duvenaud · 2019
Closest in time.
Hamiltonian graph networks with ode integrators
A. Sanchez-Gonzalez, V. Bapst, K. Cranmer, and P. Battaglia · 2019
Closest in time.
Graph wavenet for deep spatial-temporal graph modeling
Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang · 2019
Closest in time.
Continuous graph neural networks
L.-P. A. Xhonneux, M. Qu, and J. Tang · 2019
Closest in time.
S. Massaroli, M. Poli, J. Park, A. Yamashita, and H. Asama · 2020
Closest in time.