Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications.
Emergence of invariance and disentanglement in deep representations
Achille, A.; and Soatto, S. 2018 · 1980
Earlier work this paper cites.
Learning discrete structures for graph neural networks
Franceschi, L.; Niepert, M.; Pontil, M.; and He, X. 2019 · 1982
Earlier work this paper cites.
Elements of information theory
Cover, T. M. 1999 · 1999
Earlier work this paper cites.
The information bottleneck method
Tishby, N.; Pereira, F. C.; and Bialek, W. 2000 · 2000
Earlier work this paper cites.
Monte Carlo sampling methods
Shapiro, A. 2003 · 2003
Earlier work this paper cites.
Approximating the Kullback Leibler divergence between Gaussian mixture models
Hershey, J. R.; and Olsen, P. A. 2007 · 2007
Earlier work this paper cites.
Learning and generalization with the information bottleneck
Shamir, O.; Sabato, S.; and Tishby, N. 2010 · 2010
Earlier work this paper cites.
Disentangled Information Bottleneck
Pan, Z.; Niu, L.; Zhang, J.; and Zhang, L. 2020 · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
Earlier work this paper cites.
The network data repository with interactive graph analytics and visualization
Rossi, R.; and Ahmed, N. 2015 · 2015
Earlier work this paper cites.
Deep variational information bottleneck
Alemi, A. A.; Fischer, I.; Dillon, J. V.; and Murphy, K. 2016 · 2016
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N.; and Welling, M. 2016 · 2016
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Jang, E.; Gu, S.; and Poole, B. 2017 · 2017
Earlier work this paper cites.
Graph Attention Networks
Veličković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; and Bengio, Y. 2017 · 2017
Earlier work this paper cites.
Mutual information neural estimation
Belghazi, M. I.; Baratin, A.; Rajeshwar, S.; Ozair, S.; Bengio, Y.; Courville, A.; and Hjelm, D. 2018 · 2018
Cited alongside, same era.
Adversarial attack and defense on graph data: A survey
Sun, L.; Dou, Y.; Yang, C.; Wang, J.; Yu, P. S.; He, L.; and Li, B. 2018 · 2018
Cited alongside, same era.
Network representation learning: A survey
Zhang, D.; Yin, J.; Zhu, X.; and Zhang, C. 2018 · 2018
Cited alongside, same era.
Adversarial attacks on neural networks for graph data
Zügner, D.; Akbarnejad, A.; and Günnemann, S. 2018 · 2018
Cited alongside, same era.
On the information bottleneck theory of deep learning
Saxe, A. M.; Bansal, Y.; Dapello, J.; Advani, M.; Kolchinsky, A.; Tracey, B. D.; and Cox, D. D. 2019 · 2019
Cited alongside, same era.
How Powerful are Graph Neural Networks?
Xu, K.; Hu, W.; Leskovec, J.; and Jegelka, S. 2019 · 2019
Information-bottleneck approach to salient region discovery
Zhmoginov, A.; Fischer, I.; and Sandler, M. 2020 · 2020
Later among the works it cites.
Graph neural networks: A review of methods and applications
Zhou, J.; Cui, G.; Hu, S.; Zhang, Z.; Yang, C.; Liu, Z.; Wang, L.; Li, C.; and Sun, M. 2020 · 2020
Later among the works it cites.
Disentangled Variational Information Bottleneck for Multiview Representation Learning
Bao, F. 2021 · 2021
Closest in time.
Room-and-object aware knowledge reasoning for remote embodied referring expression
Gao, C.; Chen, J.; Liu, S.; Wang, L.; Zhang, Q.; and Wu, Q. 2021 · 2021
Closest in time.
IB-GAN: Disengangled Representation Learning with Information Bottleneck Generative Adversarial Networks
Jeon, I.; Lee, W.; Pyeon, M.; and Kim, G. 2021 · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Iterative deep graph learning for graph neural networks: Better and robust node embeddings
Chen, Y.; Wu, L.; and Zaki, M. 2020 · 2020
Cited alongside, same era.
Learning optimal representations with the decodable information bottleneck
Dubois, Y.; Kiela, D.; Schwab, D. J.; and Vedantam, R. 2020 · 2020
Cited alongside, same era.
A survey on text classification: From shallow to deep learning
Li, Q.; Peng, H.; Li, J.; Xia, C.; Yang, R.; Sun, L.; Yu, P. S.; and He, L. 2020 · 2020
Cited alongside, same era.
Diversity inducing Information Bottleneck in Model Ensembles
Sinha, S.; Bharadhwaj, H.; Goyal, A.; Larochelle, H.; Garg, A.; and Shkurti, F. 2020 · 2020
Cited alongside, same era.
What makes for good views for contrastive learning?
Tian, Y.; Sun, C.; Poole, B.; Krishnan, D.; Schmid, C.; and Isola, P. 2020 · 2020
Cited alongside, same era.
Graph information bottleneck
Wu, T.; Ren, H.; Li, P.; and Leskovec, J. 2020 · 2020
Cited alongside, same era.
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration
Kim, J.; Kim, M.; Woo, D.; and Kim, G. 2021 · 2021
Closest in time.
Variational Information Bottleneck for Effective Low-Resource Fine-Tuning
Mahabadi, R. K.; Belinkov, Y.; and Henderson, J. 2021 · 2021
Closest in time.
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks
Peng, H.; Zhang, R.; Dou, Y.; Yang, R.; Zhang, J.; and Yu, P. S. 2021 · 2021
Closest in time.
Directed Graph Contrastive Learning
Tong, Z.; Liang, Y.; Ding, H.; Dai, Y.; Li, X.; and Wang, C. 2021 · 2021
Closest in time.
Heterogeneous Graph Information Bottleneck
Yang, L.; Wu, F.; Zheng, Z.; Niu, B.; Gu, J.; Wang, C.; Cao, X.; and Guo, Y. 2021 · 2021
Closest in time.
Recognizing Predictive Substructures with Subgraph Information Bottleneck
Yu, J.; Xu, T.; Rong, Y.; Bian, Y.; Huang, J.; and He, R. 2021 · 2021
Closest in time.
Deep Graph Structure Learning for Robust Representations: A Survey
Zhu, Y.; Xu, W.; Zhang, J.; Liu, Q.; Wu, S.; and Wang, L. 2021 · 2021
Closest in time.
SUGAR: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism
Sun, Q.; Li, J.; Peng, H.; Wu, J.; Ning, Y.; Yu, P. S.; and He, L. 2021 · 2091
Closest in time.