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Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner, where knowledge gained from previous tasks is retained and used to aid future learning over the lifetime of the learner.
Contrast thresholds of the human eye
H. R. Blackwell · 1946
Earlier work this paper cites.
An invariant form for the prior probability in estimation problems
H. Jeffreys · 1946
Earlier work this paper cites.
A stochastic approximation method
H. Robbins and S. Monro · 1951
Earlier work this paper cites.
A new approach to linear filtering and prediction problems
R. E. Kalman · 1960
Earlier work this paper cites.
The need for biases in learning generalizations
T. M. Mitchell · 1980
Earlier work this paper cites.
Schema acquisition from one example: Psychological evidence for explanation-based learning
W.-K. Ahn, R. J. Mooney, W. F. Brewer, and G. F. DeJong · 1987
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem
M. McCloskey and N. J. Cohen · 1989
Earlier work this paper cites.
Human photoreceptor topography
C. A. Curcio, K. R. Sloan, R. E. Kalina, and A. E. Hendrickson · 1990
Earlier work this paper cites.
Sampling-based approaches to calculating marginal densities
A. E. Gelfand and A. F. Smith · 1990
Earlier work this paper cites.
Artificial neural networks
M. I. Jordan · 1990
Earlier work this paper cites.
Sensitivity analysis of discrete event systems by the “push out” method
R. Y. Rubinstein · 1992
Earlier work this paper cites.
Generative learning processes of the brain
M. C. Wittrock · 1992
Earlier work this paper cites.
Psychological studies of explanation—based learning
W.-K. Ahn and W. F. Brewer · 1993
Earlier work this paper cites.
Convolutional networks for images, speech, and time series
Y. LeCun, Y. Bengio, et al · 1995
Earlier work this paper cites.
Bayesian Learning For Neural Networks
R. M. Neal · 1995
Earlier work this paper cites.
Lifelong learning: A case study
S. Thrun · 1995
Earlier work this paper cites.
Lifelong robot learning
S. Thrun and T. M. Mitchell · 1995
Earlier work this paper cites.
Existence and uniqueness results for neural network approximations
R. C. Williamson and U. Helmke · 1995
Earlier work this paper cites.
Non-linear filtering: interacting particle resolution
P. Del Moral · 1996
Earlier work this paper cites.
Bayes risk weighted vector quantization with posterior estimation for image compression and classification
K. O. Perlmutter, S. M. Perlmutter, R. M. Gray, R. A. Olshen, and K. L. Oehler · 1996
Earlier work this paper cites.
The parallel transfer of task knowledge using dynamic learning rates based on a measure of relatedness
D. L. Silver and R. E. Mercer · 1996
Earlier work this paper cites.
Replay of neuronal firing sequences in rat hippocampus during sleep following spatial experience
W. E. Skaggs and B. L. McNaughton · 1996
Earlier work this paper cites.
Multitask learning
R. Caruana · 1997
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Polynomial bounds for vc dimension of sigmoidal and general pfaffian neural networks
M. Karpinski and A. Macintyre · 1997
Earlier work this paper cites.
Child: A first step towards continual learning
M. B. Ring · 1997
Earlier work this paper cites.
An approach to lifelong reinforcement learning through multiple environments
F. Tanaka and M. Yamamura · 1997
Earlier work this paper cites.
On-line algorithms in machine learning
A. Blum · 1998
Earlier work this paper cites.
Online learning and stochastic approximations
L. Bottou · 1998
Earlier work this paper cites.
Online algorithms: The state of the art
A. Fiat and G. J. Woeginger · 1998
Earlier work this paper cites.
Vc dimension of neural networks
E. D. Sontag · 1998
Earlier work this paper cites.
An introduction to variational methods for graphical models
M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul · 1999
Earlier work this paper cites.
A unifying review of linear gaussian models
S. Roweis and Z. Ghahramani · 1999
Earlier work this paper cites.
Online variational bayesian learning
Z. Ghahramani and H. Attias · 2000
Earlier work this paper cites.
Probability and random processes
G. Grimmett, G. R. Grimmett, D. Stirzaker, et al · 2001
Earlier work this paper cites.
The task rehearsal method of life-long learning: Overcoming impoverished data
D. L. Silver and R. E. Mercer · 2002
Earlier work this paper cites.
A bayesian approach to unsupervised one-shot learning of object categories
L. Fe-Fei et al · 2003
Earlier work this paper cites.
Bayesian clustering and product partition models
F. A. Quintana and P. L. Iglesias · 2003
Earlier work this paper cites.
Large scale online learning
L. Bottou and Y. L. Cun · 2004
Earlier work this paper cites.
Laplace propagation
E. Eskin, A. J. Smola, and S. Vishwanathan · 2004
Earlier work this paper cites.
Estimation of dependences based on empirical data
V. Vapnik · 2006
Earlier work this paper cites.
Neural ensembles in ca3 transiently encode paths forward of the animal at a decision point
A. Johnson and A. D. Redish · 2007
Earlier work this paper cites.
Incremental learning of nonparametric bayesian mixture models
R. Gomes, M. Welling, and P. Perona · 2008
Earlier work this paper cites.
The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2008
Earlier work this paper cites.
Awake replay of remote experiences in the hippocampus
M. P. Karlsson and L. M. Frank · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
Cited alongside, same era.
Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval
M. F. Carr, S. P. Jadhav, and L. M. Frank · 2011
Cited alongside, same era.
Online domain adaptation of a pre-trained cascade of classifiers
V. Jain and E. Learned-Miller · 2011
Cited alongside, same era.
Mcmc using hamiltonian dynamics
R. M. Neal et al · 2011
Cited alongside, same era.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al · 2016
Later among the works it cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi · 2016
Later among the works it cites.
A note on the evaluation of generative models
L. Theis, A. van den Oord, and M. Bethge · 2016
Later among the works it cites.
Mining aspect-specific opinion using a holistic lifelong topic model
S. Wang, Z. Chen, and B. Liu · 2016
Later among the works it cites.
Z-forcing: Training stochastic recurrent networks
A. G. A. P. Goyal, A. Sordoni, M.-A. Côté, N. R. Ke, and Y. Bengio · 2017
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Streaming variational bayes
T. Broderick, N. Boyd, A. Wibisono, A. C. Wilson, and M. I. Jordan · 2013
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Ella: An efficient lifelong learning algorithm
P. Ruvolo and E. Eaton · 2013
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Lifelong machine learning systems: Beyond learning algorithms
D. L. Silver, Q. Yang, and L. Li · 2013
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Human associative memory
J. R. Anderson and G. H. Bower · 2014
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Topic modeling using topics from many domains, lifelong learning and big data
Z. Chen and B. Liu · 2014
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Amortized inference in probabilistic reasoning
S. Gershman and N. Goodman · 2014
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M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al · 2017
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Multiplicative normalizing flows for variational bayesian neural networks
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Stick-breaking variational autoencoders
E. Nalisnick and P. Smyth · 2017
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Lifelongvae pytorch repository
J. Ramapuram · 2017
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Continual learning with deep generative replay
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Continual learning through synaptic intelligence
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Life-long disentangled representation learning with cross-domain latent homologies
A. Achille, T. Eccles, L. Matthey, C. Burgess, N. Watters, A. Lerchner, and I. Higgins · 2018
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Fixing a broken elbo
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The perception-distortion tradeoff
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Learning disentangled joint continuous and discrete representations
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Associative compression networks
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Disentangling by factorising
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Continual classification learning using generative models
F. Lavda, J. Ramapuram, M. Gregorova, and A. Kalousis · 2018
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Progressive neural architecture search
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy · 2018
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Never-ending learning
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C. V. Nguyen, Y. Li, T. D. Bui, and R. E. Turner · 2018
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Tighter variational bounds are not necessarily better
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Distribution matching in variational inference
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Assessing generative models via precision and recall
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Overcoming catastrophic forgetting with hard attention to the task
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Vae with a vampprior
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Learning attractor dynamics for generative memory
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Large scale GAN training for high fidelity natural image synthesis
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Quantifying generalization in reinforcement learning
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Monte carlo gradient estimation in machine learning
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Language models are unsupervised multitask learners
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Preventing posterior collapse with delta-vaes
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