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More than 50 years ago Bongard introduced 100 visual concept learning problems as a testbed for intelligent vision systems.
Linearly unrecognizable patterns
M. Minsky and S. Papert · 1967
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Application of Fourier Analysis to the visibility of gratings
F. W. Campbell and J. G. Robson · 1968
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Pattern Recognition
M. Bongard · 1970
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Learning structural descriptions from examples
P. H. Winston · 1970
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Syntactic Methods in Pattern Recognition
K. S. Fu · 1974
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Gödel, Escher, Bach
D. R. Hofstadter · 1977
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Syntactic Pattern Recognition: An Introduction
R. C. Gonzalez and M. G. Thomason · 1978
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Vision: A computational investigation into the human representation and processing of visual information
D. Marr · 1982
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The Logic of Perception
I. Rock · 1983
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Diagram understanding: The intersection of computer vision and graphics
F. S. Montalvo · 1985
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Recognition-by-components: A theory of human image understanding
I. Biederman · 1987
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On the proper treatment of connectionism
P. Smolensky · 1988
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What one intelligence test measures: A theoretical account of the processing in the Raven Progressive Matrices Test
M. A. Carpenter, P. A. Just and P. Shell · 1990
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A concept learning algorithm with adaptive search
K. Saito and R. Nakano · 1996
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Statistical properties of probabilistic context-free grammars
Z. Chi · 1999
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Is vision continuous with cognition? The case for cognitive impenetrability of visual perception
Z. Pylyshyn · 1999
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Minimization of boolean complexity in human concept learning
J. Feldman · 2000
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Generalization, similarity and Bayesian inference
J. B. Tenenbaum and T. L. Griffiths · 2001
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PHAEACO: A Cognitive Architecture Inspired by Bongard’s Problems
H. E. Foundalis · 2006
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A stochastic grammar of images
S.-C. Zhu and D. Mumford · 2006
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The Description Logic Handbook
F. Baader, D. Calvanese, D. L. McGuiness, D. Nardi, and P. F. Patel-Schneider, editors · 2007
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A rational analysis of rule-based concept learning
N. D. Goodman, J. B. Tenenbaum, J. Feldman, and T. L. Griffiths · 2008
Cited alongside, same era.
A grammar-based approach to visual category learning
V. Savova and J. B. Tenenbaum · 2008
Cited alongside, same era.
Teaching games: statistical sampling assumptions for learning in pedagogical situations
P. Shafto and N. D. Goodman · 2008
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Measuring elongation from shape boundary
M. Stojmenović and J. Žunić · 2008
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A multi-world approach to question answering about real-world scenes based on uncertain input
M. Malinowski and M. Fritz · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Unsupervised learning by program synthesis
K. Ellis, A. Solar-Lezama, and J. B. Tenenbaum · 2015
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From sensory signals to modality-independent conceptual representations: A probabilistic language of thought approach
G. Erdogan, I. Yildirim, and R. A. Jacobs · 2015
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Concepts in a probabilistic language of thought
N. D. Goodman, J. B. Tenenbaum, and T. Gerstenberg · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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A stochastic graph grammar for compositional object representation and recognition
L. Lin, T. Wu, J. Porway, and Z. Xu · 2009
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Fragment grammars: Exploring computation and reuse in language
T. J. O’Donnell, N. D. Goodman, and J. B. Tenenbaum · 2009
Cited alongside, same era.
Grammar-based object representations in a scene parsing task
V. Savova, F. Jäkel, and J. B. Tenenbaum · 2009
Cited alongside, same era.
Inductive logic programming
L. De Raedt · 2010
Cited alongside, same era.
Comparing machines and humans on a visual categorization test
F. Fleuret, T. Li, C. Dubout, E. K. Wampler, S. Yantis, and D. Geman · 2011
Cited alongside, same era.
Predicting pragmatic reasoning in language games
M. C. Frank and N. D. Goodman · 2012
Cited alongside, same era.
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Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
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Computer models solving intelligence test problems: Progress and implications
J. Hernández-Orallo, F. Martínez-Plumed, U. Schmid, M. Siebers, and D. L. Dowe · 2016
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The logical primitives of thought: Empirical foundations for compositional cognitive models
S. T. Piantadosi, J. B. Tenenbaum, and N. D. Goodman · 2016
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Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter · 2016
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Humans, but not deep neural networks, often miss giant targets in scenes
M. P. Eckstein, K. Koehler, L. E. Welbourne, and E. Akbas · 2017
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Visual shape perception as bayesian inference of 3d object-centered shape representations
G. Erdogan and R. A. Jacobs · 2017
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The cognitive science of sketch worksheets
K. D. Forbus, M. Chang, M. McLure, and M. Usher · 2017
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Comparing deep neural networks against humans: object recognition when the signal gets weaker
R. Geirhos, D. H. Janssen, H. H. Schütt, J. Rauber, M. Bethge, and F. A. Wichmann · 2017
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
J. Johnson, B. Hariharan, L. van der Maaten, L. Fei-Fei, C. L. Zitnick, and R. Girshick · 2017
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Learning abstract visual concepts via probabilistic program induction in a language of thought
M. C. Overlan, R. A. Jacobs, and S. T. Piantadosi · 2017
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