Fetching the paper…
Reading the bibliography…
Deep supervised learning has achieved great success in the last decade.
“cloze procedure”: A new tool for measuring readability
W. L. Taylor · 1953
Earlier work this paper cites.
Information processing in dynamical systems: Foundations of harmony theory
P. Smolensky · 1986
Earlier work this paper cites.
Modular learning in neural networks
D. H. Ballard · 1987
Earlier work this paper cites.
Learning classification with unlabeled data
V. R. de Sa · 1994
Earlier work this paper cites.
Arnetminer: extraction and mining of academic social networks
J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
M. Gutmann and A. Hyvärinen · 2010
Earlier work this paper cites.
Sparse autoencoder
A. Ng et al · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Sentiment analysis and opinion mining
B. Liu · 2012
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Y. Bengio, N. Léonard, and A. Courville · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
T. Mikolov, K. Chen, G. S. Corrado, and J. Dean · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
Earlier work this paper cites.
Domain-adversarial neural networks
H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, and M. Marchand · 2014
Earlier work this paper cites.
Nice: Non-linear independent components estimation
L. Dinh, D. Krueger, and Y. Bengio · 2014
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Deepwalk: Online learning of social representations
B. Perozzi, R. Al-Rfou, and S. Skiena · 2014
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
Earlier work this paper cites.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Earlier work this paper cites.
A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey · 2015
Earlier work this paper cites.
Masked autoencoder for distribution estimation
M. Mathieu · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
Earlier work this paper cites.
An overview of microsoft academic service (mas) and applications
A. Sinha, Z. Shen, Y. Song, H. Ma, D. Eide, B.-j. P. Hsu, and K. Wang · 2015
Earlier work this paper cites.
Line: Large-scale information network embedding
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei · 2015
Earlier work this paper cites.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
Earlier work this paper cites.
Density estimation using real nvp
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2016
Earlier work this paper cites.
J. Donahue, P. Krähenbühl, and T. Darrell · 2016
Earlier work this paper cites.
Adversarially learned inference
V. Dumoulin, I. Belghazi, B. Poole, O. Mastropietro, A. Lamb, M. Arjovsky, and A. Courville · 2016
Earlier work this paper cites.
Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
Earlier work this paper cites.
node2vec: Scalable feature learning for networks
A. Grover and J. Leskovec · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 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.
Variational graph auto-encoders
T. N. Kipf and M. Welling · 2016
Earlier work this paper cites.
Learning representations for automatic colorization
G. Larsson, M. Maire, and G. Shakhnarovich · 2016
Earlier work this paper cites.
Unsupervised visual representation learning by graph-based consistent constraints
D. Li, W.-C. Hung, J.-B. Huang, S. Wang, N. Ahuja, and M.-H. Yang · 2016
Earlier work this paper cites.
Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
Earlier work this paper cites.
f-gan: Training generative neural samplers using variational divergence minimization
S. Nowozin, B. Cseke, and R. Tomioka · 2016
Earlier work this paper cites.
Context encoders: Feature learning by inpainting
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros · 2016
Earlier work this paper cites.
Squad: 100,000+ questions for machine comprehension of text
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang · 2016
Earlier work this paper cites.
” why should i trust you?” explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
A. Van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves, et al · 2016
Earlier work this paper cites.
Pixel recurrent neural networks
A. Van Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
Earlier work this paper cites.
Joint unsupervised learning of deep representations and image clusters
J. Yang, D. Parikh, and D. Batra · 2016
Earlier work this paper cites.
Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
Earlier work this paper cites.
A. A. Alemi, B. Poole, I. Fischer, J. V. Dillon, R. A. Saurous, and K. Murphy · 2017
Earlier work this paper cites.
Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
Earlier work this paper cites.
Enriching word vectors with subword information
P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov · 2017
Earlier work this paper cites.
Kbgan: Adversarial learning for knowledge graph embeddings
L. Cai and W. Y. Wang · 2017
Earlier work this paper cites.
On sampling strategies for neural network-based collaborative filtering
T. Chen, Y. Sun, Y. Shi, and L. Hong · 2017
Earlier work this paper cites.
Triple generative adversarial nets
L. Chongxuan, T. Xu, J. Zhu, and B. Zhang · 2017
Earlier work this paper cites.
Word translation without parallel data
A. Conneau, G. Lample, M. Ranzato, L. Denoyer, and H. Jégou · 2017
Earlier work this paper cites.
Densely connected convolutional networks
G. Huang, Z. Liu, and K. Q. Weinberger · 2017
Cited alongside, same era.
Globally and locally consistent image completion
S. Iizuka, E. Simo-Serra, and H. Ishikawa · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
Cited alongside, same era.
Colorization as a proxy task for visual understanding
G. Larsson, M. Maire, and G. Shakhnarovich · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al · 2017
Cited alongside, same era.
struc2vec: Learning node representations from structural identity
L. F. Ribeiro, P. H. Saverese, and D. R. Figueiredo · 2017
A mutual information maximization perspective of language representation learning
L. Kong, C. d. M. d’Autume, W. Ling, L. Yu, Z. Dai, and D. Yogatama · 2019
Later among the works it cites.
Albert: A lite bert for self-supervised learning of language representations
Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut · 2019
Later among the works it cites.
Roberta: A robustly optimized bert pretraining approach
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov · 2019
Later among the works it cites.
Self-supervised learning of pretext-invariant representations
I. Misra and L. van der Maaten · 2019
Later among the works it cites.
Molecularrnn: Generating realistic molecular graphs with optimized properties
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Adversarial representation learning for domain adaptation
J. Shen, Y. Qu, W. Zhang, and Y. Yu · 2017
Cited alongside, same era.
Style transfer from non-parallel text by cross-alignment
T. Shen, T. Lei, R. Barzilay, and T. Jaakkola · 2017
Cited alongside, same era.
Neural discrete representation learning
A. van den Oord, O. Vinyals, et al · 2017
Cited alongside, same era.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. 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.
Split-brain autoencoders: Unsupervised learning by cross-channel prediction
R. Zhang, P. Isola, and A. A. Efros · 2017
Cited alongside, same era.
M. Popova, M. Shvets, J. Oliva, and O. Isayev · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever · 2019
Later among the works it cites.
Generating diverse high-fidelity images with vq-vae-2
A. Razavi, A. van den Oord, and O. Vinyals · 2019
Later among the works it cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
N. Reimers and I. Gurevych · 2019
Later among the works it cites.
Adversarial representation learning for text-to-image matching
N. Sarafianos, X. Xu, and I. A. Kakadiaris · 2019
Later among the works it cites.
F.-Y. Sun, J. Hoffmann, and J. Tang · 2019
Later among the works it cites.
vgraph: A generative model for joint community detection and node representation learning
F.-Y. Sun, M. Qu, J. Hoffmann, C.-W. Huang, and J. Tang · 2019
Later among the works it cites.
Multi-stage self-supervised learning for graph convolutional networks
K. Sun, Z. Zhu, and Z. Lin · 2019
Later among the works it cites.
Ernie: Enhanced representation through knowledge integration
Y. Sun, S. Wang, Y. Li, S. Feng, X. Chen, H. Zhang, X. Tian, D. Zhu, H. Tian, and H. Wu · 2019
Later among the works it cites.
Y. Tian, D. Krishnan, and P. Isola · 2019
Later among the works it cites.
On mutual information maximization for representation learning
M. Tschannen, J. Djolonga, P. K. Rubenstein, S. Gelly, and M. Lucic · 2019
Later among the works it cites.
Generative adversarial networks: A survey and taxonomy
Z. Wang, Q. She, and T. E. Ward · 2019
Later among the works it cites.
Iterative reorganization with weak spatial constraints: Solving arbitrary jigsaw puzzles for unsupervised representation learning
C. Wei, L. Xie, X. Ren, Y. Xia, C. Su, J. Liu, Q. Tian, and A. L. Yuille · 2019
Later among the works it cites.
Pretrained encyclopedia: Weakly supervised knowledge-pretrained language model
W. Xiong, J. Du, W. Y. Wang, and V. Stoyanov · 2019
Later among the works it cites.
Clusterfit: Improving generalization of visual representations
X. Yan, I. Misra, A. Gupta, D. Ghadiyaram, and D. Mahajan · 2019
Later among the works it cites.
Xlnet: Generalized autoregressive pretraining for language understanding
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le · 2019
Later among the works it cites.
Oag: Toward linking large-scale heterogeneous entity graphs
F. Zhang, X. Liu, J. Tang, Y. Dong, P. Yao, J. Zhang, X. Gu, Y. Wang, B. Shao, R. Li, and K. Wang · 2019
Later among the works it cites.
Prone: fast and scalable network representation learning
J. Zhang, Y. Dong, Y. Wang, J. Tang, and M. Ding · 2019
Later among the works it cites.
Ernie: Enhanced language representation with informative entities
Z. Zhang, X. Han, Z. Liu, X. Jiang, M. Sun, and Q. Liu · 2019
Later among the works it cites.
Local aggregation for unsupervised learning of visual embeddings
C. Zhuang, A. L. Zhai, and D. Yamins · 2019
Later among the works it cites.
Unsupervised learning of visual features by contrasting cluster assignments
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin · 2020
Closest in time.
A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
Closest in time.
Big self-supervised models are strong semi-supervised learners
T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. Hinton · 2020
Closest in time.
Improved baselines with momentum contrastive learning
X. Chen, H. Fan, R. Girshick, and K. He · 2020
Closest in time.
Exploring simple siamese representation learning
X. Chen and K. He · 2020
Closest in time.
Electra: Pre-training text encoders as discriminators rather than generators
K. Clark, M.-T. Luong, Q. V. Le, and C. D. Manning · 2020
Closest in time.
Bootstrap your own latent: A new approach to self-supervised learning
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. D. Guo, M. G. Azar, et al · 2020
Closest in time.
Realm: Retrieval-augmented language model pre-training
K. Guu, K. Lee, Z. Tung, P. Pasupat, and M.-W. Chang · 2020
Closest in time.
Contrastive multi-view representation learning on graphs
K. Hassani and A. H. Khasahmadi · 2020
Closest in time.
Gpt-gnn: Generative pre-training of graph neural networks
Z. Hu, Y. Dong, K. Wang, K.-W. Chang, and Y. Sun · 2020
Closest in time.
Heterogeneous graph transformer
Z. Hu, Y. Dong, K. Wang, and Y. Sun · 2020
Closest in time.
Spanbert: Improving pre-training by representing and predicting spans
M. Joshi, D. Chen, Y. Liu, D. S. Weld, L. Zettlemoyer, and O. Levy · 2020
Closest in time.
Dense passage retrieval for open-domain question answering
V. Karpukhin, B. Oğuz, S. Min, L. Wu, S. Edunov, D. Chen, and W.-t. Yih · 2020
Closest in time.
Representation learning via invariant causal mechanisms
J. Mitrovic, B. McWilliams, J. Walker, L. Buesing, and C. Blundell · 2020
Closest in time.
How useful is self-supervised pretraining for visual tasks?
A. Newell and J. Deng · 2020
Closest in time.
Self-supervised graph representation learning via global context prediction
Z. Peng, Y. Dong, M. Luo, X. ming Wu, and Q. Zheng · 2020
Closest in time.
Self-supervised graph representation learning via global context prediction
Z. Peng, Y. Dong, M. Luo, X.-M. Wu, and Q. Zheng · 2020
Closest in time.
Gcc: Graph contrastive coding for graph neural network pre-training
J. Qiu, Q. Chen, Y. Dong, J. Zhang, H. Yang, M. Ding, K. Wang, and J. Tang · 2020
Closest in time.
Pre-trained models for natural language processing: A survey
X. Qiu, T. Sun, Y. Xu, Y. Shao, N. Dai, and X. Huang · 2020
Closest in time.
Graphaf: a flow-based autoregressive model for molecular graph generation
C. Shi, M. Xu, Z. Zhu, W. Zhang, M. Zhang, and J. Tang · 2020
Closest in time.
Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes
K. Sun, Z. Lin, and Z. Zhu · 2020
Closest in time.
What makes for good views for contrastive learning
Y. Tian, C. Sun, B. Poole, D. Krishnan, C. Schmid, and P. Isola · 2020
Closest in time.
T. Wang and P. Isola · 2020
Closest in time.
Self-training with noisy student improves imagenet classification
Q. Xie, M.-T. Luong, E. Hovy, and Q. V. Le · 2020
Closest in time.
How neural networks extrapolate: From feedforward to graph neural networks
K. Xu, J. Li, M. Zhang, S. S. Du, K.-i. Kawarabayashi, and S. Jegelka · 2020
Closest in time.
Graph contrastive learning with augmentations
Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen · 2020
Closest in time.
When does self-supervision help graph convolutional networks?
Y. You, T. Chen, Z. Wang, and Y. Shen · 2020
Closest in time.
Rethinking pre-training and self-training
B. Zoph, G. Ghiasi, T.-Y. Lin, Y. Cui, H. Liu, E. D. Cubuk, and Q. V. Le · 2020
Closest in time.
Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2020
Closest in time.
Self-supervised pretraining of visual features in the wild
P. Goyal, M. Caron, B. Lefaudeux, M. Xu, P. Wang, V. Pai, M. Singh, V. Liptchinsky, I. Misra, A. Joulin, and P. Bojanowski · 2021
Closest in time.