Fetching the paper…
Reading the bibliography…
Unsupervised and self-supervised learning approaches have become a crucial tool to learn representations for downstream prediction tasks.
A theory of the learnable
L. G. Valiant · 1984
Earlier work this paper cites.
Information processing in dynamical systems: foundations of harmony theory
P. Smolensky · 1986
Earlier work this paper cites.
Learning Internal Representations by Error Propagation
D. E. Rumelhart and J. L. McClelland · 1987
Earlier work this paper cites.
A model of inductive bias learning
J. Baxter · 2000
Earlier work this paper cites.
A global geometric framework for nonlinear dimensionality reduction
J. B. Tenenbaum, V. d. Silva, and J. C. Langford · 2000
Earlier work this paper cites.
Algorithms for manifold learning
L. Cayton · 2005
Earlier work this paper cites.
Automatically constructing a corpus of sentential paraphrases
W. B. Dolan and C. Brockett · 2005
Earlier work this paper cites.
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
O. Chapelle, B. Schölkopf, and A. Zien · 2006
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
G. E. Hinton and R. R. Salakhutdinov · 2006
Earlier work this paper cites.
Improving robot navigation through self-supervised online learning
B. Sofman, E. Lin, J. Bagnell, J. Cole, N. Vandapel, and A. Stentz · 2006
Earlier work this paper cites.
Classification using discriminative restricted boltzmann machines
H. Larochelle and Y. Bengio · 2008
Earlier work this paper cites.
Sparse representations for image classification: Learning discriminative and reconstructive non-parametric dictionaries
F. Rodriguez and G. Sapiro · 2008
Earlier work this paper cites.
Visualizing data using t-SNE
L. van der Maaten and G. Hinton · 2008
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol · 2008
Earlier work this paper cites.
Supervised dictionary learning
J. Mairal, J. Ponce, G. Sapiro, A. Zisserman, and F. R. Bach · 2009
Earlier work this paper cites.
A discriminative model for semi-supervised learning
M.-F. Balcan and A. Blum · 2010
Earlier work this paper cites.
A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2010
Earlier work this paper cites.
Why does unsupervised pre-training help deep learning?
D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio · 2010
Earlier work this paper cites.
Autoencoders, unsupervised learning and deep architectures
P. Baldi · 2011
Earlier work this paper cites.
The manifold tangent classifier
S. Rifai, Y. N. Dauphin, P. Vincent, Y. Bengio, and X. Muller · 2011
Earlier work this paper cites.
On the hardness of domain adaptation and the utility of unlabeled target samples
S. Ben-David and R. Urner · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives, 2012
Y. Bengio, A. Courville, and P. Vincent · 2012
Cited alongside, same era.
Learning algorithms for the classification restricted boltzmann machine
H. Larochelle, M. Mandel, R. Pascanu, and Y. Bengio · 2012
Cited alongside, same era.
Dictionary learning for sparse representation: A novel approach
M. Sadeghi, M. Babaie-Zadeh, and C. Jutten · 2013
Cited alongside, same era.
Discriminative unsupervised feature learning with convolutional neural networks
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox · 2014
Cited alongside, same era.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
In search of the real inductive bias: On the role of implicit regularization in deep learning
Split-brain autoencoders: Unsupervised learning by cross-channel prediction
R. Zhang, P. Isola, and A. Efros · 2017
Later among the works it cites.
Can we gain more from orthogonality regularizations in training deep cnns?
N. Bansal, X. Chen, and Z. Wang · 2018
Later among the works it cites.
Unsupervised representation learning by predicting image rotations
S. Gidaris, P. Singh, and N. Komodakis · 2018
Later among the works it cites.
Grasp2vec: Learning object representations from self-supervised grasping
E. Jang, C. Devin, V. Vanhoucke, and S. Levine · 2018
Later among the works it cites.
Supervised autoencoders: Improving generalization performance with unsupervised regularizers
L. Le, A. Patterson, and M. White · 2018
Later among the works it cites.
Algorithmic regularization in over-parameterized matrix sensing and neural networks with quadratic activations
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
B. Neyshabur, R. Tomioka, and N. Srebro · 2014
Cited alongside, same era.
Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
Cited alongside, same era.
Deep metric learning using triplet network
E. Hoffer and N. Ailon · 2015
Cited alongside, same era.
Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
L. Pinto and A. Gupta · 2015
Cited alongside, same era.
Learning to poke by poking: Experiential learning of intuitive physics
P. Agrawal, A. Nair, P. Abbeel, J. Malik, and S. Levine · 2016
Cited alongside, same era.
A non-generative framework and convex relaxations for unsupervised learning
E. Hazan and T. Ma · 2016
Cited alongside, same era.
Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
Cited alongside, same era.
Y. Li, T. Ma, and H. Zhang · 2018
Later among the works it cites.
Foundations of machine learning
M. Mohri, A. Rostamizadeh, and A. Talwalkar · 2018
Later among the works it cites.
High-dimensional probability: An introduction with applications in data science
R. Vershynin · 2018
Later among the works it cites.
Learning representations by maximizing mutual information across views
P. Bachman, R. D. Hjelm, and W. Buchwalter · 2019
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
Later among the works it cites.
Data-efficient image recognition with contrastive predictive coding, 2019
O. J. Hénaff, A. Srinivas, J. D. Fauw, A. Razavi, C. Doersch, S. M. A. Eslami, and A. van den Oord · 2019
Later among the works it cites.
Revealing the dark secrets of BERT
O. Kovaleva, A. Romanov, A. Rogers, and A. Rumshisky · 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.
Uniform convergence may be unable to explain generalization in deep learning
V. Nagarajan and J. Z. Kolter · 2019
Later among the works it cites.
Pac-bayesian contrastive unsupervised representation learning, 2019
K. Nozawa, P. Germain, and B. Guedj · 2019
Later among the works it cites.
A theoretical analysis of contrastive unsupervised representation learning
N. Saunshi, O. Plevrakis, S. Arora, M. Khodak, and H. Khandeparkar · 2019
Later among the works it cites.
Few-shot learning via learning the representation, provably
S. S. Du, W. Hu, S. M. Kakade, J. D. Lee, and Q. Lei · 2020
Closest in time.
A no-free-lunch theorem for multitask learning
S. Hanneke and S. Kpotufe · 2020
Closest in time.
On the theory of transfer learning: The importance of task diversity
N. Tripuraneni, M. I. Jordan, and C. Jin · 2020
Closest in time.
On mutual information maximization for representation learning
M. Tschannen, J. Djolonga, P. K. Rubenstein, S. Gelly, and M. Lucic · 2020
Closest in time.