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Sorting and ranking supervision is a method for training neural networks end-to-end based on ordering constraints.
Sorting networks and their applications
Batcher, K. E · 1968
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An 0(n log n) sorting network
Ajtai, M., Komlós, J., and Szemerédi, E · 1983
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The Art of Computer Programming, Volume 3: (2nd Ed.) Sorting and Searching
Knuth, D. E · 1998
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Gputerasort: high performance graphics co-processor sorting for large database management
Govindaraju, N. K., Gray, J., Kumar, R., and Manocha, D · 2006
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Spiking neural p systems – a natural model for sorting networks
Ceterchi, R. and Tomescu, A. I · 2008
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Mnist handwritten digit database
LeCun, Y., Cortes, C., and Burges, C · 2010
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Ranking via sinkhorn propagation
Adams, R. P. and Zemel, R. S · 2011
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Learning to rank for information retrieval
Liu, T.-Y · 2011
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Designing sorting networks: A new paradigm
Baddar, S. W. A.-H. and Batcher, K. E · 2012
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Multi-digit number recognition from street view imagery using deep convolutional neural networks
Goodfellow, I. J., Bulatov, Y., Ibarz, J., Arnoud, S., and Shet, V · 2013
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Graves, A., Wayne, G., and Danihelka, I · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
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Learning latent permutations with gumbel-sinkhorn networks
Mena, G., Belanger, D., Linderman, S., and Snoek, J · 2018
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Evolutionary development of growing generic sorting networks by means of rewriting systems
Bidlo, M. and Dobeš, M · 2019
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Differentiable ranking and sorting using optimal transport
Cuturi, M., Teboul, O., and Vert, J.-P · 2019
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A hybrid cpu gpu approach for optimizing sorting throughput
Gowanlock, M. and Karsin, B · 2019
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Stochastic Optimization of Sorting Networks via Continuous Relaxations
Grover, A., Wang, E., Zweig, A., and Ermon, S · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Metta, V. P. and Kelemenova, A · 2015
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A box-constrained approach for hard permutation problems
Lim, C. H. and Wright, S · 2016
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Order matters: Sequence to sequence for sets
Vinyals, O., Bengio, S., and Kudlur, M · 2016
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Fast Differentiable Sorting and Ranking
Blondel, M., Teboul, O., Berthet, Q., and Djolonga, J · 2020
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Differentiable top-k with optimal transport
Xie, Y., Dai, H., Chen, M., Dai, B., Zhao, T., Zha, H., Wei, W., and Pfister, T · 2020
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