Fetching the paper…
Reading the bibliography…
Contrastive learning has become a key component of self-supervised learning approaches for graph-structured data.
Sun, F.-Y., Hoffmann, J., Verma, V., and Tang, J. (2019) · 1908
Earlier work this paper cites.
A generalized probability density function for double-bounded random processes
Kumaraswamy, P. (1980) · 1980
Earlier work this paper cites.
What makes for good views for contrastive learning?
Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., and Isola, P. (2020) · 2005
Earlier work this paper cites.
Deep graph contrastive representation learning
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L. (2020) · 2006
Earlier work this paper cites.
Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T. (2008) · 2008
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y. (2010) · 2010
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
Earlier work this paper cites.
GloVe: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. (2014) · 2014
Earlier work this paper cites.
Deepwalk: Online learning of social representations
Perozzi, B., Al-Rfou, R., and Skiena, S. (2014) · 2014
Earlier work this paper cites.
Image-based recommendations on styles and substitutes
McAuley, J., Targett, C., Shi, Q., and Van Den Hengel, A. (2015) · 2015
Earlier work this paper cites.
An overview of microsoft academic service (mas) and applications
Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B.-J. P., and Wang, K. (2015) · 2015
Cited alongside, same era.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z. (2016) · 2016
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Grover, A. and Leskovec, J. (2016) · 2016
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B. (2016) · 2016
Cited alongside, same era.
Variational graph auto-encoders
Kipf, T. N. and Welling, M. (2016) · 2016
Cited alongside, same era.
Concrete dropout
Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E. (2019) · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019) · 2019
Later among the works it cites.
Deep graph infomax
Veličković, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D. (2019) · 2019
Later among the works it cites.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020) · 2020
Later among the works it cites.
Bootstrap your own latent - a new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., Piot, B., kavukcuoglu, k., Munos, R., and Valko, M. (2020) · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gal, Y., Hron, J., and Kendall, A. (2017) · 2017
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y. (2017) · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M. (2017) · 2017
Cited alongside, same era.
Evaluating bayesian deep learning methods for semantic segmentation
Mukhoti, J. and Gal, Y. (2018) · 2018
Cited alongside, same era.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Chiang, W.-L., Liu, X., Si, S., Li, Y., Bengio, S., and Hsieh, C.-J. (2019) · 2019
Cited alongside, same era.
Bayesian graph neural networks with adaptive connection sampling
Hasanzadeh, A., Hajiramezanali, E., Boluki, S., Zhou, M., Duffield, N., Narayanan, K., and Qian, X. (2020) · 2020
Later among the works it cites.
Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y. (2020) · 2020
Later among the works it cites.
Infonce is a variational autoencoder
Aitchison, L. (2021) · 2021
Closest in time.
Bootstrapped representation learning on graphs
Thakoor, S., Tallec, C., Azar, M. G., Munos, R., Veličković, P., and Valko, M. (2021) · 2021
Closest in time.
Graph contrastive learning automated
You, Y., Chen, T., Shen, Y., and Wang, Z. (2021) · 2021
Closest in time.