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Abstract Visual Reasoning (AVR) problems are commonly used to approximate human intelligence.
J. C. Raven, “Mental tests used in genetic studies: The performance of related individuals on tests mainly educative and mainly reproductive,” Master’s thesis, University of London , 1936
1936
Earlier work this paper cites.
R. C. Casperson, “The visual discrimination of geometric forms.” Journal of Experimental Psychology , vol. 40, no. 5, p. 668, 1950
1950
Earlier work this paper cites.
J. J. Gibson and E. J. Gibson, “Perceptual learning: Differentiation or enrichment?” Psychological review , vol. 62, no. 1, p. 32, 1955
1955
Earlier work this paper cites.
J. S. Bruner, “On perceptual readiness.” Psychological review , vol. 64, no. 2, p. 123, 1957
1957
Earlier work this paper cites.
D. G. Bobrow, “Natural language input for a computer problem solving system,” 1964
1964
Earlier work this paper cites.
T. G. Evans, “A heuristic program to solve geometric-analogy problems,” in Proceedings of the April 21-23, 1964, spring joint computer conference , 1964, pp. 327–338
1964
Earlier work this paper cites.
M. M. Bongard, “The recognition problem,” Foreign Technology Div Wright-Patterson AFB Ohio, Tech. Rep., 1968
1968
Earlier work this paper cites.
E. S. Gollin and M. Schadler, “Relational learning and transfer by young children,” Journal of Experimental Child Psychology , vol. 14, no. 2, pp. 219–232, 1972
1972
Earlier work this paper cites.
T. Zentall, D. Hogan, and J. Holder, “Comparison of two oddity tasks with pigeons,” Learning and Motivation , vol. 5, no. 1, pp. 106–117, 1974
1974
Earlier work this paper cites.
H. Abelson, N. Goodman, and L. Rudolph, “Logo manual,” 1974
1974
Earlier work this paper cites.
T. R. Zentall, D. E. Hogan, and C. A. Edwards, “Oddity learning in the pigeon: Effect of negative instances, correction, and number of incorrect alternatives,” Animal Learning & Behavior , vol. 8, no. 4, pp. 621–629, 1980
1980
Earlier work this paper cites.
D. Gentner, “Structure-mapping: A theoretical framework for analogy,” Cognitive science , vol. 7, no. 2, pp. 155–170, 1983
1983
Earlier work this paper cites.
R. E. Snow, P. C. Kyllonen, and B. Marshalek, “The topography of ability and learning correlations,” Advances in the psychology of human intelligence , vol. 2, no. S 47, p. 103, 1984
1984
Earlier work this paper cites.
I. M. Pepperberg, “Acquisition of the same/different concept by an african grey parrot (psittacus erithacus): Learning with respect to categories of color, shape, and material,” Animal Learning & Behavior , vol. 15, no. 4, pp. 423–432, 1987
1987
Earlier work this paper cites.
R. Catrambone and K. J. Holyoak, “Overcoming contextual limitations on problem-solving transfer.” Journal of Experimental Psychology: Learning, Memory, and Cognition , vol. 15, no. 6, p. 1147, 1989
1989
Earlier work this paper cites.
P. A. Carpenter, M. A. Just, and P. Shell, “What one intelligence test measures: a theoretical account of the processing in the Raven Progressive Matrices Test.” Psychological review , vol. 97, no. 3, p. 404, 1990
1990
Earlier work this paper cites.
Y. LeCun, B. E. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. E. Hubbard, and L. D. Jackel, “Handwritten digit recognition with a back-propagation network,” in Advances in neural information processing systems , 1990, pp. 396–404
1990
Earlier work this paper cites.
D. L. Oden, R. K. Thompson, and D. Premack, “Infant chimpanzees spontaneously perceive both concrete and abstract same/different relations,” Child development , vol. 61, no. 3, pp. 621–631, 1990
1990
Earlier work this paper cites.
K. Wynn, “Addition and subtraction by human infants,” Nature , vol. 358, no. 6389, pp. 749–750, 1992
1992
Earlier work this paper cites.
M. L. Gick and K. Paterson, “Do contrasting examples facilitate schema acquisition and analogical transfer?” Canadian Journal of Psychology/Revue canadienne de psychologie , vol. 46, no. 4, p. 539, 1992
1992
Earlier work this paper cites.
M. L. Gick and S. J. McGarry, “Learning from mistakes: Inducing analogous solution failures to a source problem produces later successes in analogical transfer.” Journal of Experimental Psychology: Learning, Memory, and Cognition , vol. 18, no. 3, p. 623, 1992
1992
Earlier work this paper cites.
D. Gentner and A. B. Markman, “Structural alignment in comparison: No difference without similarity,” Psychological science , vol. 5, no. 3, pp. 152–158, 1994
1994
Earlier work this paper cites.
D. R. Hofstadter, Fluid concepts and creative analogies: Computer models of the fundamental mechanisms of thought. Basic books, 1995
1995
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
R. Caruana, “Multitask learning,” Machine learning , vol. 28, no. 1, pp. 41–75, 1997
1997
Earlier work this paper cites.
J. C. Raven and J. H. Court, Raven’s progressive matrices and vocabulary scales . Oxford pyschologists Press Oxford, England, 1998
1998
Earlier work this paper cites.
E. Temple and M. I. Posner, “Brain mechanisms of quantity are similar in 5-year-old children and adults,” Proceedings of the National Academy of Sciences , vol. 95, no. 13, pp. 7836–7841, 1998
1998
Earlier work this paper cites.
D. Gentner and V. Gunn, “Structural alignment facilitates the noticing of differences,” Memory & cognition , vol. 29, no. 4, pp. 565–577, 2001
2001
Earlier work this paper cites.
R. Primi, “Complexity of geometric inductive reasoning tasks: Contribution to the understanding of fluid intelligence,” Intelligence , vol. 30, no. 1, pp. 41–70, 2001
2001
Earlier work this paper cites.
M. Bassok, “Analogical transfer in problem solving,” The psychology of problem solving , pp. 343–369, 2003
2003
Earlier work this paper cites.
M. Gardner, D. J. Albers, and P. L. Renz, Martin Gardner’s mathematical games: the entire collection of his scientific American columns . Mathematical Association of America, 2005
2005
Earlier work this paper cites.
M. Gardner and D. Richards, The colossal book of short puzzles and problems . Norton, 2006
2006
Earlier work this paper cites.
S. Dehaene, V. Izard, P. Pica, and E. Spelke, “Core knowledge of geometry in an amazonian indigene group,” Science , vol. 311, no. 5759, pp. 381–384, 2006
2006
Earlier work this paper cites.
A. A. Wright and J. S. Katz, “Mechanisms of same/different concept learning in primates and avians,” Behavioural processes , vol. 72, no. 3, pp. 234–254, 2006
2006
Earlier work this paper cites.
H. E. Foundalis, “Phaeaco: A cognitive architecture inspired by bongard’s problems.” PhD dissertation, Indiana University, 2006
2006
Earlier work this paper cites.
G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” science , vol. 313, no. 5786, pp. 504–507, 2006
2006
Earlier work this paper cites.
A. Lovett, K. Forbus, and J. Usher, “Analogy with qualitative spatial representations can simulate solving raven’s progressive matrices,” in Proceedings of the Annual Meeting of the Cognitive Science Society , vol. 29, no. 29, 2007
2007
Earlier work this paper cites.
A. Lovett, K. Lockwood, and K. Forbus, “Modeling cross-cultural performance on the visual oddity task,” in International Conference on Spatial Cognition . Springer, 2008, pp. 378–393
2008
Earlier work this paper cites.
A. Mukherjee and U. Garain, “A review of methods for automatic understanding of natural language mathematical problems,” Artificial Intelligence Review , vol. 29, no. 2, pp. 93–122, 2008
2008
Earlier work this paper cites.
A. Lovett, K. Lockwood, and K. Forbus, “A computational model of the visual oddity task,” in Proceedings of the Annual Meeting of the Cognitive Science Society , vol. 30, no. 30, 2008
2008
Earlier work this paper cites.
L. E. Matzen, Z. O. Benz, K. R. Dixon, J. Posey, J. K. Kroger, and A. E. Speed, “Recreating raven’s: Software for systematically generating large numbers of raven-like matrix problems with normed properties,” Behavior research methods , vol. 42, no. 2, pp. 525–541, 2010
2010
Earlier work this paper cites.
A. Lovett, K. Forbus, and J. Usher, “A structure-mapping model of raven’s progressive matrices,” in Proceedings of the Annual Meeting of the Cognitive Science Society , vol. 32, no. 32, 2010
2010
Earlier work this paper cites.
K. McGreggor, M. Kunda, and A. Goel, “A fractal analogy approach to the raven’s test of intelligence,” in Workshops at the Twenty-Fourth AAAI Conference on Artificial Intelligence , 2010
2010
Earlier work this paper cites.
M. Kunda, K. McGreggor, and A. Goel, “Taking a look (literally!) at the raven’s intelligence test: Two visual solution strategies,” in Proceedings of the Annual Meeting of the Cognitive Science Society , vol. 32, no. 32, 2010
2010
Earlier work this paper cites.
M. Gutmann and A. Hyvärinen, “Noise-contrastive estimation: A new estimation principle for unnormalized statistical models,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics . JMLR Workshop and Conference Proceedings, 2010, pp. 297–304
2010
Earlier work this paper cites.
J. Sinapov and A. Stoytchev, “The odd one out task: Toward an intelligence test for robots,” in 2010 IEEE 9th International Conference on Development and Learning . IEEE, 2010, pp. 126–131
2010
Earlier work this paper cites.
P. E. Ruiz, “Building and solving odd-one-out classification problems: A systematic approach,” Intelligence , vol. 39, no. 5, pp. 342–350, 2011
2011
Earlier work this paper cites.
F. Fleuret, T. Li, C. Dubout, E. K. Wampler, S. Yantis, and D. Geman, “Comparing machines and humans on a visual categorization test,” Proceedings of the National Academy of Sciences , vol. 108, no. 43, pp. 17 621–17 625, 2011
2011
Earlier work this paper cites.
A. Lovett and K. Forbus, “Cultural commonalities and differences in spatial problem-solving: A computational analysis,” Cognition , vol. 121, no. 2, pp. 281–287, 2011
2011
Earlier work this paper cites.
S. Dehaene, The number sense: How the mind creates mathematics . OUP USA, 2011
2011
Earlier work this paper cites.
K. McGreggor and A. Goel, “Fractally finding the odd one out: an analogical strategy for noticing novelty,” in 2011 AAAI Fall Symposium Series , 2011
2011
Earlier work this paper cites.
——, “Finding the odd one out: a fractal analogical approach,” in Proceedings of the 8th ACM conference on Creativity and cognition , 2011, pp. 289–298
2011
Earlier work this paper cites.
K. Smets and J. Vreeken, “The odd one out: Identifying and characterising anomalies,” in Proceedings of the 2011 SIAM international conference on data mining . SIAM, 2011, pp. 804–815
2011
Earlier work this paper cites.
H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre, “Hmdb: a large video database for human motion recognition,” in 2011 International conference on computer vision . IEEE, 2011, pp. 2556–2563
2011
Earlier work this paper cites.
J. Moses, “Macsyma: A personal history,” pp. 123–130, 2012
2012
Earlier work this paper cites.
A. Lovett and K. Forbus, “Modeling multiple strategies for solving geometric analogy problems,” in Proceedings of the Annual Meeting of the Cognitive Science Society , vol. 34, no. 34, 2012
2012
Earlier work this paper cites.
M. Kunda, K. McGreggor, and A. Goel, “Reasoning on the raven’s advanced progressive matrices test with iconic visual representations,” in Proceedings of the Annual Meeting of the Cognitive Science Society , vol. 34, no. 34, 2012
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in International Conference on Learning Representations (ICLR) , 2013
2013
Earlier work this paper cites.
N. Kushman, Y. Artzi, L. Zettlemoyer, and R. Barzilay, “Learning to automatically solve algebra word problems,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2014, pp. 271–281
2014
Earlier work this paper cites.
W. Zaremba, K. Kurach, and R. Fergus, “Learning to discover efficient mathematical identities,” Advances in Neural Information Processing Systems , vol. 27, pp. 1278–1286, 2014
2014
Cited alongside, same era.
M. J. Hosseini, H. Hajishirzi, O. Etzioni, and N. Kushman, “Learning to solve arithmetic word problems with verb categorization,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2014, pp. 523–533
2014
Cited alongside, same era.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , vol. 27, 2014
2014
Cited alongside, same era.
E. Schubert, A. Zimek, and H.-P. Kriegel, “Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection,” Data mining and knowledge discovery , vol. 28, no. 1, pp. 190–237, 2014
2014
2019
Later among the works it cites.
M. Z. Hossain, F. Sohel, M. F. Shiratuddin, and H. Laga, “A comprehensive survey of deep learning for image captioning,” ACM Computing Surveys (CsUR) , vol. 51, no. 6, pp. 1–36, 2019
2019
Later among the works it cites.
K. Zheng, Z.-J. Zha, and W. Wei, “Abstract reasoning with distracting features,” in Advances in Neural Information Processing Systems , 2019, pp. 5842–5853
2019
Later among the works it cites.
Y. Li, L. Kaiser, S. Bengio, and S. Si, “Area attention,” in International Conference on Machine Learning . PMLR, 2019, pp. 3846–3855
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
D. L. Yamins, H. Hong, C. F. Cadieu, E. A. Solomon, D. Seibert, and J. J. DiCarlo, “Performance-optimized hierarchical models predict neural responses in higher visual cortex,” Proceedings of the national academy of sciences , vol. 111, no. 23, pp. 8619–8624, 2014
2014
Cited alongside, same era.
S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. L. Zitnick, and D. Parikh, “VQA: Visual question answering,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2425–2433
2015
Cited alongside, same era.
K. Wang and Z. Su, “Automatic generation of raven’s progressive matrices,” in Twenty-Fourth International Joint Conference on Artificial Intelligence , 2015
2015
Cited alongside, same era.
K. Ellis, A. Solar-Lezama, and J. Tenenbaum, “Unsupervised learning by program synthesis,” in Advances in Neural Information Processing Systems , vol. 28, 2015
2015
Cited alongside, same era.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning . PMLR, 2015, pp. 448–456
2015
Cited alongside, same era.
2015
Cited alongside, same era.
N. Kriegeskorte, “Deep neural networks: a new framework for modeling biological vision and brain information processing,” Annual review of vision science , vol. 1, pp. 417–446, 2015
2015
Cited alongside, same era.
J. Hernández-Orallo, F. Martínez-Plumed, U. Schmid, M. Siebers, and D. L. Dowe, “Computer models solving intelligence test problems: Progress and implications,” Artificial Intelligence , vol. 230, pp. 74–107, 2016
2016
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
N. Ilinykh, S. Zarrieß, and D. Schlangen, “Tell me more: A dataset of visual scene description sequences,” in Proceedings of the 12th international conference on natural language generation , 2019, pp. 152–157
2019
Later among the works it cites.
L. Mou, Y. Hua, and X. X. Zhu, “A relation-augmented fully convolutional network for semantic segmentation in aerial scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 416–12 425
2019
Later among the works it cites.
W. Zhang, C. Zhang, Y. Zhu, and S.-C. Zhu, “Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning,” in AAAI Conference on Artificial Intelligence (AAAI) , 2020
2020
Later among the works it cites.
T. Webb, Z. Dulberg, S. Frankland, A. Petrov, R. O’Reilly, and J. Cohen, “Learning representations that support extrapolation,” in International Conference on Machine Learning . PMLR, 2020, pp. 10 136–10 146
2020
Later among the works it cites.
W. Nie, Z. Yu, L. Mao, A. B. Patel, Y. Zhu, and A. Anandkumar, “Bongard-logo: A new benchmark for human-level concept learning and reasoning,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Later among the works it cites.
S. Manmadhan and B. C. Kovoor, “Visual question answering: a state-of-the-art review,” Artificial Intelligence Review , vol. 53, no. 8, pp. 5705–5745, 2020
2020
Later among the works it cites.
Y. Srivastava, V. Murali, S. R. Dubey, and S. Mukherjee, “Visual question answering using deep learning: A survey and performance analysis,” in International Conference on Computer Vision and Image Processing . Springer, 2020, pp. 75–86
2020
Later among the works it cites.
K. R. Allen, K. A. Smith, and J. B. Tenenbaum, “Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning,” Proceedings of the National Academy of Sciences , vol. 117, no. 47, pp. 29 302–29 310, 2020
2020
Later among the works it cites.
R. Riochet, M. Y. Castro, M. Bernard, A. Lerer, R. Fergus, V. Izard, and E. Dupoux, “Intphys: A framework and benchmark for visual intuitive physics reasoning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
Later among the works it cites.
F. Baradel, N. Neverova, J. Mille, G. Mori, and C. Wolf, “CoPhy: Counterfactual Learning of Physical Dynamics,” in International Conference on Learning Representations (ICLR) , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
X. Yun, T. A. Bohn, and C. X. Ling, “A deeper look at bongard problems.” in Canadian Conference on AI , 2020, pp. 528–539
2020
Later among the works it cites.
A. Raghu, M. Raghu, S. Bengio, and O. Vinyals, “Rapid learning or feature reuse? towards understanding the effectiveness of maml,” in International Conference on Learning Representations , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
N. Pekar, Y. Benny, and L. Wolf, “Generating correct answers for progressive matrices intelligence tests,” in Advances in Neural Information Processing Systems , vol. 33, 2020, pp. 7390–7400
2020
Later among the works it cites.
S. Spratley, K. Ehinger, and T. Miller, “A closer look at generalisation in raven,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVII 16 . Springer, 2020, pp. 601–616
2020
Later among the works it cites.
G. Lample and F. Charton, “Deep learning for symbolic mathematics,” in International Conference on Learning Representations , 2020
2020
Later among the works it cites.
D. Wang, M. Jamnik, and P. Lio, “Abstract diagrammatic reasoning with multiplex graph networks,” in International Conference on Learning Representations (ICLR) , 2020
2020
Later among the works it cites.
M. Shanahan, K. Nikiforou, A. Creswell, C. Kaplanis, D. Barrett, and M. Garnelo, “An explicitly relational neural network architecture,” in International Conference on Machine Learning . PMLR, 2020, pp. 8593–8603
2020
Later among the works it cites.
M. Jahrens and T. Martinetz, “Solving raven’s progressive matrices with multi-layer relation networks,” in 2020 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2020, pp. 1–6
2020
Later among the works it cites.
T. Hua and M. Kunda, “Modeling gestalt visual reasoning on raven’s progressive matrices using generative image inpainting techniques.” in CogSci , 2020
2020
Later among the works it cites.
Y. Kim, J. Shin, E. Yang, and S. J. Hwang, “Few-shot visual reasoning with meta-analogical contrastive learning,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Later among the works it cites.
M. Patacchiola and A. J. Storkey, “Self-supervised relational reasoning for representation learning,” in Advances in Neural Information Processing Systems , vol. 33, 2020, pp. 4003–4014
2020
Later among the works it cites.
2020
Later among the works it cites.
D. Teney, P. Wang, J. Cao, L. Liu, C. Shen, and A. van den Hengel, “V-PROM: A benchmark for visual reasoning using visual progressive matrices,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 12 071–12 078
2020
Later among the works it cites.
2020
Later among the works it cites.
R. Fjelland, “Why general artificial intelligence will not be realized,” Humanities and Social Sciences Communications , vol. 7, no. 1, pp. 1–9, 2020
2020
Later among the works it cites.
2021
Later among the works it cites.
H. L. van der Maas, L. Snoek, and C. E. Stevenson, “How much intelligence is there in artificial intelligence? a 2020 update,” Intelligence , vol. 87, p. 101548, 2021
2021
Later among the works it cites.
S. Stabinger, D. Peer, J. Piater, and A. Rodríguez-Sánchez, “Evaluating the progress of deep learning for visual relational concepts,” Journal of Vision , vol. 21, no. 11, pp. 8–8, 2021
2021
Later among the works it cites.
S. Hu, Y. Ma, X. Liu, Y. Wei, and S. Bai, “Stratified rule-aware network for abstract visual reasoning,” in AAAI Conference on Artificial Intelligence (AAAI) , 2021
2021
Later among the works it cites.
T. Zhuo and M. Kankanhalli, “Effective abstract reasoning with dual-contrast network,” in International Conference on Learning Representations (ICLR) , 2021
2021
Later among the works it cites.
H. E. Foundalis, “Index of bongard problems,” http://www.foundalis.com/res/bps/bpidx.htm , 2006, accessed: 2021-07-04
2021
Later among the works it cites.
S. Kharagorgiev, “Solving bongard problems with deep learning,” https://k10v.github.io/2018/02/25/Solving-Bongard-problems-with-deep-learning/ , Feb. 2018, accessed: 2021-07-04
2021
Later among the works it cites.
M. Ricci, R. Cadene, and T. Serre, “Same-different conceptualization: a machine vision perspective,” Current Opinion in Behavioral Sciences , vol. 37, pp. 47–55, 2021
2021
Later among the works it cites.
K. D. Forbus and A. Lovett, “Same/different in visual reasoning,” Current Opinion in Behavioral Sciences , vol. 37, pp. 63–68, 2021
2021
Later among the works it cites.
N. Messina, G. Amato, F. Carrara, C. Gennaro, and F. Falchi, “Solving the same-different task with convolutional neural networks,” Pattern Recognition Letters , vol. 143, pp. 75–80, 2021
2021
Later among the works it cites.
C. M. Funke, J. Borowski, K. Stosio, W. Brendel, T. S. Wallis, and M. Bethge, “Five points to check when comparing visual perception in humans and machines,” Journal of Vision , vol. 21, no. 3, pp. 16–16, 2021
2021
Later among the works it cites.
C. Zhang, B. Jia, S.-C. Zhu, and Y. Zhu, “Abstract spatial-temporal reasoning via probabilistic abduction and execution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9736–9746
2021
Later among the works it cites.
2021
Later among the works it cites.
W. Li, L. Yu, Y. Wu, and L. C. Paulson, “Isarstep: a benchmark for high-level mathematical reasoning,” in International Conference on Learning Representations , 2021
2021
Later among the works it cites.
N. Rahaman, M. W. Gondal, S. Joshi, P. Gehler, Y. Bengio, F. Locatello, and B. Schölkopf, “Dynamic inference with neural interpreters,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Later among the works it cites.
S. Minaee, Y. Y. Boykov, F. Porikli, A. J. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image segmentation using deep learning: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
Later among the works it cites.
J. Gui, Z. Sun, Y. Wen, D. Tao, and J. Ye, “A review on generative adversarial networks: Algorithms, theory, and applications,” IEEE Transactions on Knowledge and Data Engineering , 2021
2021
Later among the works it cites.
A. Jabbar, X. Li, and B. Omar, “A survey on generative adversarial networks: Variants, applications, and training,” ACM Computing Surveys (CSUR) , vol. 54, no. 8, pp. 1–49, 2021
2021
Later among the works it cites.
F. Shi, B. Li, and X. Xue, “Raven’s progressive matrices completion with latent gaussian process priors,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 11, 2021, pp. 9612–9620
2021
Later among the works it cites.
H. Wei, Z. Li, F. Huang, C. Zhang, H. Ma, and Z. Shi, “Integrating scene semantic knowledge into image captioning,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) , vol. 17, no. 2, pp. 1–22, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2022
Closest in time.
P. Tomaszewska, A. Żychowski, and J. Mańdziuk, “Duel-based deep learning system for solving IQ tests,” in International Conference on Artificial Intelligence and Statistics (AISTATS) , 2022
2022
Closest in time.