Fetching the paper…
Reading the bibliography…
In supervised machine learning, an agent is typically trained once and then deployed.
Random sampling with a reservoir
J. S. Vitter · 1985
Earlier work this paper cites.
Using fast weights to deblur old memories
G. E. Hinton and D. C. Plaut · 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.
Artmap: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network
G. A. Carpenter, S. Grossberg, and J. H. Reynolds · 1991
Earlier work this paper cites.
Fuzzy artmap: A neural network architecture for incremental supervised learning of analog multidimensional maps
G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen · 1992
Earlier work this paper cites.
Self-improving reactive agents based on reinforcement learning, planning and teaching
L.-J. Lin · 1992
Earlier work this paper cites.
Stacked generalization
D. H. Wolpert · 1992
Earlier work this paper cites.
The weighted majority algorithm
N. Littlestone and M. K. Warmuth · 1994
Earlier work this paper cites.
Learning vector quantization
T. Kohonen · 1995
Earlier work this paper cites.
Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
J. L. McClelland, B. L. McNaughton, and R. C. O’reilly · 1995
Earlier work this paper cites.
Gaussian artmap: A neural network for fast incremental learning of noisy multidimensional maps
J. R. Williamson · 1996
Earlier work this paper cites.
Birch: an efficient data clustering method for very large databases
T. Zhang, R. Ramakrishnan, and M. Livny · 1996
Earlier work this paper cites.
Memory consolidation, retrograde amnesia and the hippocampal complex
L. Nadel and M. Moscovitch · 1997
Earlier work this paper cites.
Birch: A new data clustering algorithm and its applications
T. Zhang, R. Ramakrishnan, and M. Livny · 1997
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
R. M. French · 1999
Earlier work this paper cites.
Mining high-speed data streams
P. Domingos and G. Hulten · 2000
Earlier work this paper cites.
Scalability for clustering algorithms revisited
F. Farnstrom, J. Lewis, and C. Elkan · 2000
Earlier work this paper cites.
Mining time-changing data streams
G. Hulten, L. Spencer, and P. Domingos · 2001
Earlier work this paper cites.
Learn++: An incremental learning algorithm for supervised neural networks
R. Polikar, L. Upda, S. S. Upda, and V. Honavar · 2001
Earlier work this paper cites.
A framework for clustering evolving data streams
C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu · 2003
Earlier work this paper cites.
Clustering data streams: Theory and practice
S. Guha, A. Meyerson, N. Mishra, R. Motwani, and L. O’Callaghan · 2003
Earlier work this paper cites.
Efficient decision tree construction on streaming data
R. Jin and G. Agrawal · 2003
Earlier work this paper cites.
Incremental optics: Efficient computation of updates in a hierarchical cluster ordering
H.-P. Kriegel, P. Kröoger, and I. Gotlibovich · 2003
Earlier work this paper cites.
Mining concept-drifting data streams using ensemble classifiers
H. Wang, W. Fan, P. S. Yu, and J. Han · 2003
Earlier work this paper cites.
A framework for projected clustering of high dimensional data streams
C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu · 2004
Earlier work this paper cites.
On demand classification of data streams
C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu · 2004
Cited alongside, same era.
Memory retention–the synaptic stability versus plasticity dilemma
W. C. Abraham and A. Robins · 2005
Cited alongside, same era.
Density-based clustering over an evolving data stream with noise
F. Cao, M. Estert, W. Qian, and A. Zhou · 2006
Cited alongside, same era.
Decision trees for mining data streams
J. Gama, R. Fernandes, and R. Rocha · 2006
Cited alongside, same era.
St-dbscan: An algorithm for clustering spatial–temporal data
D. Birant and A. Kut · 2007
Cited alongside, same era.
Density-based clustering for real-time stream data
Y. Chen and L. Tu · 2007
Cited alongside, same era.
Se-stream: Dimension projection for evolution-based clustering of high dimensional data streams
R. Chairukwattana, T. Kangkachit, T. Rakthanmanon, and K. Waiyamai · 2014
Later among the works it cites.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
Later among the works it cites.
A bio-inspired incremental learning architecture for applied perceptual problems
A. Gepperth and C. Karaoguz · 2016
Later among the works it cites.
Clustering data streams based on shared density between micro-clusters
M. Hahsler and M. Bolaños · 2016
Later among the works it cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
Learning without forgetting
Z. Li and D. Hoiem · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
W. Dai, Q. Yang, G.-R. Xue, and Y. Yu · 2007
Cited alongside, same era.
A survey of classification methods in data streams
M. M. Gaber, A. Zaslavsky, and S. Krishnaswamy · 2007
Cited alongside, same era.
Dynamic weighted majority: An ensemble method for drifting concepts
J. Z. Kolter and M. A. Maloof · 2007
Cited alongside, same era.
New options for hoeffding trees
B. Pfahringer, G. Holmes, and R. Kirkby · 2007
Cited alongside, same era.
Adaptive learning from evolving data streams
A. Bifet and R. Gavaldà · 2009
Cited alongside, same era.
New ensemble methods for evolving data streams
A. Bifet, G. Holmes, B. Pfahringer, R. Kirkby, and R. Gavaldà · 2009
Cited alongside, same era.
Later among the works it cites.
An algorithm for online k-means clustering
E. Liberty, R. Sriharsha, and M. Sviridenko · 2016
Later among the works it cites.
Pathnet: Evolution channels gradient descent in super neural networks
C. Fernando, D. Banarse, C. Blundell, Y. Zwols, D. Ha, A. A. Rusu, A. Pritzel, and D. Wierstra · 2017
Later among the works it cites.
Adaptive random forests for evolving data stream classification
H. M. Gomes, A. Bifet, J. Read, J. P. Barddal, F. Enembreck, B. Pfharinger, G. Holmes, and T. Abdessalem · 2017
Later among the works it cites.
Introduction to stream: An extensible framework for data stream clustering research with R
M. Hahsler, M. Bolaños, and J. Forrest · 2017
Later among the works it cites.
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, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell · 2017
Later among the works it cites.
Ensemble learning for data stream analysis: A survey
B. Krawczyk, L. L. Minku, J. Gama, J. Stefanowski, and M. Woźniak · 2017
Later among the works it cites.
Core50: a new dataset and benchmark for continuous object recognition
V. Lomonaco and D. Maltoni · 2017
Later among the works it cites.
Gradient episodic memory for continual learning
D. Lopez-Paz et al · 2017
Later among the works it cites.
icarl: Incremental classifier and representation learning
S.-A. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert · 2017
Later among the works it cites.
Life-long learning based on dynamic combination model
B. Ren, H. Wang, J. Li, and H. Gao · 2017
Later among the works it cites.
Scalable real-time classification of data streams with concept drift
M. Tennant, F. Stahl, O. Rana, and J. B. Gomes · 2017
Later among the works it cites.
Continual learning through synaptic intelligence
F. Zenke, B. Poole, and S. Ganguli · 2017
Later among the works it cites.
stream: Infrastructure for Data Stream Mining
M. Hahsler, M. Bolaños, and J. Forrest · 2018
Closest in time.
streamMOA: Interface for MOA Stream Clustering Algorithms
M. Hahsler, M. Bolaños, and J. Forrest · 2018
Closest in time.
Fearnet: Brain-inspired model for incremental learning
R. Kemker and C. Kanan · 2018
Closest in time.
Measuring catastrophic forgetting in neural networks
R. Kemker, M. McClure, A. Abitino, T. L. Hayes, and C. Kanan · 2018
Closest in time.
Combination of information entropy and ensemble classification for detecting concept drift in data stream
O. A. Mahdi, E. Pardede, and J. Cao · 2018
Closest in time.
Continuous learning in single-incremental-task scenarios
D. Maltoni and V. Lomonaco · 2018
Closest in time.
C. Manapragada, G. Webb, and M. Salehi · 2018
Closest in time.