Fetching the paper…
Reading the bibliography…
Abstract visual reasoning (AVR) domain encompasses problems solving which requires the ability to reason about relations among entities present in a given scene.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Advances in Neural Information Processing Systems , vol. 33, 2020, pp. 1877–1901
1901
Earlier work this paper cites.
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.
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.
K. S. Fu, Syntactic methods in pattern recognition . Elsevier, 1974
1974
Earlier work this paper cites.
D. R. Hofstadter et al. , Gödel, Escher, Bach: an eternal golden braid . Basic books New York, 1979, vol. 13
1979
Earlier work this paper cites.
D. Gentner, “The structure of analogical models in science.” BOLT BERANEK AND NEWMAN INC CAMBRIDGE MA, Tech. Rep., 1980
1980
Earlier work this paper cites.
A. K. Jain and B. Chandrasekaran, “39 dimensionality and sample size considerations in pattern recognition practice,” Handbook of statistics , vol. 2, pp. 835–855, 1982
1982
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.
D. K. Detterman and R. J. Sternberg, What is intelligence?: Contemporary viewpoints on its nature and definition . Ablex, 1986
1986
Earlier work this paper cites.
B. Falkenhainer, K. D. Forbus, and D. Gentner, The structure-mapping engine . Department of Computer Science, University of Illinois at Urbana-Champaign, 1986, vol. 1275
1986
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.
S. J. Raudys, A. K. Jain et al. , “Small sample size effects in statistical pattern recognition: Recommendations for practitioners,” IEEE Transactions on pattern analysis and machine intelligence , vol. 13, no. 3, pp. 252–264, 1991
1991
Earlier work this paper cites.
R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Machine learning , vol. 8, no. 3, pp. 229–256, 1992
1992
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.
H. Borko and R. T. Putnam, “Learning to teach,” in International Conference on Learning Representations (ICLR) , 1996
1996
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.
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.
H. E. Foundalis, “Phaeaco: A cognitive architecture inspired by bongard’s problems.” PhD dissertation, Indiana University, 2006
2006
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. Legg, M. Hutter et al. , “A collection of definitions of intelligence,” Frontiers in Artificial Intelligence and applications , vol. 157, p. 17, 2007
2007
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.
S.-C. Zhu and D. Mumford, A stochastic grammar of images . Now Publishers Inc, 2007
2007
Earlier work this paper cites.
S. M. Jaeggi, M. Buschkuehl, J. Jonides, and W. J. Perrig, “Improving fluid intelligence with training on working memory,” Proceedings of the National Academy of Sciences , vol. 105, no. 19, pp. 6829–6833, 2008
2008
Earlier work this paper cites.
L. Lin, T. Wu, J. Porway, and Z. Xu, “A stochastic graph grammar for compositional object representation and recognition,” Pattern Recognition , vol. 42, no. 7, pp. 1297–1307, 2009
2009
Earlier work this paper cites.
Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” in Proceedings of the 26th annual international conference on machine learning , 2009, pp. 41–48
2009
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 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.
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.
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. P. Kumar, B. Packer, and D. Koller, “Self-paced learning for latent variable models.” in Advances in Neural Information Processing Systems , vol. 1, 2010, p. 2
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.
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.
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.
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.
T. Malisiewicz, A. Gupta, and A. A. Efros, “Ensemble of exemplar-svms for object detection and beyond,” in 2011 International conference on computer vision . IEEE, 2011, pp. 89–96
2011
Earlier work this paper cites.
——, “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.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems , vol. 25, pp. 1097–1105, 2012
2012
Earlier work this paper cites.
C. Strannegård, S. Cirillo, and V. Ström, “An anthropomorphic method for progressive matrix problems,” Cognitive Systems Research , vol. 22, pp. 35–46, 2013
2013
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.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Advances in Neural Information Processing Systems , vol. 26, 2013
2013
Earlier work this paper cites.
K. McGreggor and A. Goel, “Confident reasoning on raven’s progressive matrices tests,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 28, no. 1, 2014
2014
Earlier work this paper cites.
M. Ragni and S. Neubert, “Analyzing raven’s intelligence test: Cognitive model, demand, and complexity,” in Computational Approaches to Analogical Reasoning: Current Trends . Springer, 2014, pp. 351–370
2014
Earlier work this paper cites.
L. Smith and D. Gentner, “The role of difference-detection in learning contrastive categories,” in Proceedings of the Annual Meeting of the Cognitive Science Society , vol. 36, 2014
2014
Earlier work this paper cites.
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
Earlier work this paper cites.
M. Malinowski and M. Fritz, “A multi-world approach to question answering about real-world scenes based on uncertain input,” Advances in neural information processing systems , vol. 27, pp. 1682–1690, 2014
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Earlier work this paper cites.
K. Wang and Z. Su, “Automatic generation of raven’s progressive matrices,” in Twenty-Fourth International Joint Conference on Artificial Intelligence , 2015
2015
Earlier work this paper cites.
T. Ko, V. Peddinti, D. Povey, and S. Khudanpur, “Audio augmentation for speech recognition,” in Sixteenth Annual Conference of the International Speech Communication Association , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
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
Earlier work this paper cites.
H. Gao, J. Mao, J. Zhou, Z. Huang, L. Wang, and W. Xu, “Are you talking to a machine? dataset and methods for multilingual image question answering,” in Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 , ser. NIPS’15, 2015, p. 2296–2304
2015
Cited alongside, same era.
M. Ren, R. Kiros, and R. Zemel, “Image question answering: A visual semantic embedding model and a new dataset,” in Advances in Neural Information Processing Systems , vol. 1, no. 2, 2015, p. 5
2015
Cited alongside, same era.
——, “Exploring models and data for image question answering,” in Advances in Neural Information Processing Systems , vol. 28, 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.
Y. Chen, M. Rohrbach, Z. Yan, Y. Shuicheng, J. Feng, and Y. Kalantidis, “Graph-based global reasoning networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 433–442
2019
Later among the works it cites.
N. Saunshi, O. Plevrakis, S. Arora, M. Khodak, and H. Khandeparkar, “A theoretical analysis of contrastive unsupervised representation learning,” in International Conference on Machine Learning . PMLR, 2019, pp. 5628–5637
2019
Later among the works it cites.
S. Schneider, A. Baevski, R. Collobert, and M. Auli, “wav2vec: Unsupervised Pre-Training for Speech Recognition,” in Proc. Interspeech , 2019, pp. 3465–3469
2019
Later among the works it cites.
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. Sohn, “Improved deep metric learning with multi-class n-pair loss objective,” in Proceedings of the 30th International Conference on Neural Information Processing Systems , 2016, pp. 1857–1865
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
S. M. A. Eslami, N. Heess, T. Weber, Y. Tassa, D. Szepesvari, k. kavukcuoglu, and G. E. Hinton, “Attend, infer, repeat: Fast scene understanding with generative models,” in Advances in Neural Information Processing Systems , vol. 29, 2016
2016
Cited alongside, same era.
N. Sarafianos, B. Boteanu, B. Ionescu, and I. A. Kakadiaris, “3d human pose estimation: A review of the literature and analysis of covariates,” Computer Vision and Image Understanding , vol. 152, pp. 1–20, 2016
2016
Cited alongside, same era.
P. Battaglia, R. Pascanu, M. Lai, D. Jimenez Rezende, and k. kavukcuoglu, “Interaction networks for learning about objects, relations and physics,” in Advances in Neural Information Processing Systems , vol. 29, 2016
2016
Cited alongside, same era.
A. Hyvärinen and H. Morioka, “Unsupervised feature extraction by time-contrastive learning and nonlinear ica,” in Advances in Neural Information Processing Systems , vol. 29, 2016
2016
Cited alongside, same era.
J. Hernández-Orallo, The measure of all minds: evaluating natural and artificial intelligence . Cambridge University Press, 2017
2017
Cited alongside, same era.
D. Hoshen and M. Werman, “IQ of Neural Networks,” arXiv preprint arXiv:1710.01692 , 2017
2017
Cited alongside, same era.
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.
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.
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.
2020
Later among the works it cites.
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.
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 . Springer, 2020, pp. 601–616
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.
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. 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.
2020
Later among the works it cites.
R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann, “Shortcut learning in deep neural networks,” Nature Machine Intelligence , vol. 2, no. 11, pp. 665–673, 2020
2020
Later among the works it cites.
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, “Supervised contrastive learning,” in Advances in Neural Information Processing Systems , vol. 33, 2020, pp. 18 661–18 673
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.
Y. You, J. Li, S. Reddi, J. Hseu, S. Kumar, S. Bhojanapalli, X. Song, J. Demmel, K. Keutzer, and C.-J. Hsieh, “Large batch optimization for deep learning: Training bert in 76 minutes,” in International Conference on Learning Representations , 2020
2020
Later among the works it cites.
O. S. Kayhan and J. C. v. Gemert, “On translation invariance in cnns: Convolutional layers can exploit absolute spatial location,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 14 274–14 285
2020
Later among the works it cites.
A. Kolesnikov, L. Beyer, X. Zhai, J. Puigcerver, J. Yung, S. Gelly, and N. Houlsby, “Big transfer (bit): General visual representation learning,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part V 16 . Springer, 2020, pp. 491–507
2020
Later among the works it cites.
Y. Zhu, T. Gao, L. Fan, S. Huang, M. Edmonds, H. Liu, F. Gao, C. Zhang, S. Qi, Y. N. Wu et al. , “Dark, beyond deep: A paradigm shift to cognitive ai with humanlike common sense,” Engineering , vol. 6, no. 3, pp. 310–345, 2020
2020
Later among the works it cites.
Y. Chen, Y. Tian, and M. He, “Monocular human pose estimation: A survey of deep learning-based methods,” Computer Vision and Image Understanding , vol. 192, p. 102897, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
S. Hao, Y. Zhou, and Y. Guo, “A brief survey on semantic segmentation with deep learning,” Neurocomputing , vol. 406, pp. 302–321, 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.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning . PMLR, 2020, pp. 1597–1607
2020
Later among the works it cites.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9729–9738
2020
Later among the works it cites.
T. Klein and M. Nabi, “Contrastive self-supervised learning for commonsense reasoning,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , jul 2020, pp. 7517–7523
2020
Later among the works it cites.
F. Kreuk, J. Keshet, and Y. Adi, “Self-supervised contrastive learning for unsupervised phoneme segmentation,” in Proc. Interspeech , 2020
2020
Later among the works it cites.
T. Kipf, E. van der Pol, and M. Welling, “Contrastive learning of structured world models,” in International Conference on Learning Representations , 2020
2020
Later among the works it cites.
M. Laskin, A. Srinivas, and P. Abbeel, “Curl: Contrastive unsupervised representations for reinforcement learning,” in International Conference on Machine Learning . PMLR, 2020, pp. 5639–5650
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. 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.
Y. Benny, N. Pekar, and L. Wolf, “Scale-localized abstract reasoning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 12 557–12 565
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.
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.
T. Zhuo, Q. Huang, and M. Kankanhalli, “Unsupervised abstract reasoning for raven’s problem matrices,” IEEE Transactions on Image Processing , vol. 30, pp. 8332–8341, 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.
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.
2021
Later among the works it cites.
2021
Later among the works it cites.
Z. Wang, Q. She, and T. E. Ward, “Generative adversarial networks in computer vision: A survey and taxonomy,” ACM Comput. Surv. , vol. 54, no. 2, Feb. 2021
2021
Later among the works it cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2021
2021
Later among the works it cites.
A. Jaegle, F. Gimeno, A. Brock, O. Vinyals, A. Zisserman, and J. Carreira, “Perceiver: General perception with iterative attention,” in Proceedings of the 38th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, vol. 139. PMLR, 18–24 Jul 2021, pp. 4651–4664
2021
Later among the works it cites.
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.
H. Al-Tahan and Y. Mohsenzadeh, “Clar: Contrastive learning of auditory representations,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2021, pp. 2530–2538
2021
Later among the works it cites.
G. Liu, C. Zhang, L. Zhao, T. Qin, J. Zhu, L. Jian, N. Yu, and T.-Y. Liu, “Return-based contrastive representation learning for reinforcement learning,” in International Conference on Learning Representations , 2021
2021
Later among the works it cites.