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Convolutional neural networks (CNNs) have obtained astounding successes for important pattern recognition tasks, but they suffer from high computational complexity and the lack of interpretability.
Some aspects of the sequential design of experiments
H. Robbins · 1952
Earlier work this paper cites.
On behaviour of finite automata in random medium
M. L. Tsetlin · 1961
Earlier work this paper cites.
Bandit processes and dynamic allocation indices
J. Gittins · 1979
Earlier work this paper cites.
A Theory of the Learnable
L. G. Valiant · 1984
Earlier work this paper cites.
Deterministic Learning Automata Solutions to The Equipartitioning Problem
B. J. Oommen and D. C. Ma · 1988
Earlier work this paper cites.
Learning Automata: An Introduction
K. S. Narendra and M. A. L. Thathachar · 1989
Earlier work this paper cites.
Mining association rules between sets of items in large databases
R. Agrawal, T. Imieliński, and A. Swami · 1993
Earlier work this paper cites.
Using Finite State Automata to Produce Self-Optimization and Self-Control
B. Tung and L. Kleinrock · 1996
Earlier work this paper cites.
Stochastic Searching on the Line and its Applications to Parameter Learning in Nonlinear Optimization
B. J. Oommen · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, et al · 1998
Earlier work this paper cites.
Solving the Satisfiability Problem Using Finite Learning Automata
O.-C. Granmo and N. Bouhmala · 2007
Earlier work this paper cites.
Learning Automata-based Solutions to the Nonlinear Fractional Knapsack Problem with Applications to Optimal Resource Allocation
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Earlier work this paper cites.
Routing bandwidth-guaranteed paths in MPLS traffic engineering: A multiple race track learning approach
B. Oommen, S. Misra, and O.-C. Granmo · 2007
Earlier work this paper cites.
A solution to the stochastic point location problem in metalevel nonstationary environments
B. J. Oommen, S.-W. Kim, M. T. Samuel, and O.-C. Granmo · 2008
Earlier work this paper cites.
Hardness of Approximate Two-Level Logic Minimization and PAC Learning with Membership Queries
V. Feldman · 2009
Earlier work this paper cites.
Stochastic Learning for SAT-Encoded Graph Coloring Problems
N. Bouhmala and O.-C. Granmo · 2010
Earlier work this paper cites.
Optimal sampling for estimation with constrained resources using a learning automaton-based solution for the nonlinear fractional knapsack problem
O. C. Granmo and B. J. Oommen · 2010
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Solving Stochastic Nonlinear Resource Allocation Problems Using a Hierarchy of Twofold Resource Allocation Automata
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Disjunctions of Conjunctions, Cognitive Simplicity, and Consideration Sets
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Service selection in stochastic environments: A learning-automaton based solution
A. Yazidi, O.-C. Granmo, and B. Oommen · 2012
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BNN+: Improved Binary Network Training
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Later among the works it cites.
A streaming sampling algorithm for social activity networks using fixed structure learning automata
M. Ghavipour and M. R. Meybodi · 2018
Later among the works it cites.
O.-C. Granmo · 2018
Later among the works it cites.
Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
Later among the works it cites.
An intriguing failing of convolutional neural networks and the coordconv solution
R. Liu, J. Lehman, P. Molino, F. P. Such, E. Frank, A. Sergeev, and J. Yosinski · 2018
Later among the works it cites.
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Learning theory analysis for association rules and sequential event prediction
C. Rudin, B. Letham, and D. Madigan · 2013
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Learning-Automaton-Based Online Discovery and Tracking of Spatiotemporal Event Patterns
A. Yazidi, O.-C. Granmo, and B. Oommen · 2013
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A two-armed bandit collective for hierarchical examplar based mining of frequent itemsets with applications to intrusion detection
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Visualizing and understanding convolutional networks
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Symmetrical Hierarchical Stochastic Searching on the Line in Informative and Deceptive Environments
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Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
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