Fetching the paper…
Reading the bibliography…
Contrastive learning is a family of self-supervised methods where a model is trained to solve a classification task constructed from unlabeled data.
Criteria for recurrence and existence of invariant measures for multidimensional diffusions
R. Bhattacharya et al · 1978
Earlier work this paper cites.
Rates of convergence for empirical processes of stationary mixing sequences
B. Yu · 1994
Earlier work this paper cites.
Lan property for ergodic diffusions with discrete observations
E. Gobet · 2002
Earlier work this paper cites.
Contrastive estimation reveals topic posterior information to linear models
C. Tosh, A. Krishnamurthy, and D. Hsu · 2003
Earlier work this paper cites.
Transition density estimation for stochastic differential equations via forward-reverse representations
G. N. Milstein, J. G. Schoenmakers, V. Spokoiny, et al · 2004
Earlier work this paper cites.
Closed-form likelihood expansions for multivariate diffusions
Y. Ait-Sahalia et al · 2008
Earlier work this paper cites.
Rademacher complexity bounds for non-iid processes
M. Mohri and A. Rostamizadeh · 2009
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
M. Gutmann and A. Hyvärinen · 2010
Cited alongside, same era.
Foundations of machine learning
M. Mohri, A. Rostamizadeh, and A. Talwalkar · 2012
Cited alongside, same era.
Unsupervised feature extraction by time-contrastive learning and nonlinear ica
A. Hyvarinen and H. Morioka · 2016
Cited alongside, same era.
Nonlinear ica of temporally dependent stationary sources
A. Hyvarinen and H. Morioka · 2017
Cited alongside, same era.
Sampling from a log-concave distribution with projected langevin monte carlo
S. Bubeck, R. Eldan, and J. Lehec · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
Later among the works it cites.
Flow contrastive estimation of energy-based models
R. Gao, E. Nijkamp, D. P. Kingma, Z. Xu, A. M. Dai, and Y. N. Wu · 2020
Later among the works it cites.
Demystifying contrastive self-supervised learning: Invariances, augmentations and dataset biases
S. Purushwalkam and A. Gupta · 2020
Later among the works it cites.
Telescoping density-ratio estimation
B. Rhodes, K. Xu, and M. U. Gutmann · 2020
Later among the works it cites.
What makes for good views for contrastive learning
Y. Tian, C. Sun, B. Poole, D. Krishnan, C. Schmid, and P. Isola · 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…
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
Cited alongside, same era.
A theoretical analysis of contrastive unsupervised representation learning
N. Saunshi, O. Plevrakis, S. Arora, M. Khodak, and H. Khandeparkar · 2019
Cited alongside, same era.
Contrastive learning, multi-view redundancy, and linear models, 2020b
C. Tosh, A. Krishnamurthy, and D. Hsu
Cited in the paper.
T. Wang and P. Isola · 2020
Later among the works it cites.