Koo, T., Globerson, A., Carreras Pérez, X., and Collins, M. (2007). Structured prediction models via the matrix-tree theorem. In Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 141-150.
Krizhevsky, A., Hinton, G., et al. (2009). Learning multiple layers of features from tiny images.
Kruskal, J. B. (1956). On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical Society, 7(1):48-50.
Kumar, A., Brazil, G., and Liu, X. (2021). Groomed-nms: Grouped mathematically differentiable nms for monocular 3d object detection. In Proceedings of the Conference on Computer Vision and Pattern Recognition, pages 8973-8983.
Le Lidec, Q., Laptev, I., Schmid, C., and Carpentier, J. (2021). Differentiable rendering with perturbed optimizers. Advances in Neural Information Processing Systems, 34:20398-20409.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324.
LeCun, Y., Cortes, C., and Burges, C. (2010). Mnist handwritten digit database. ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist, 2.
Llinares-López, F., Berthet, Q., Blondel, M., Teboul, O., and Vert, J.-P. (2023). Deep embedding and alignment of protein sequences. Nature Methods, 20(1):104-111.
Maddison, C. J., Mnih, A., and Teh, Y. W. (2016). The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712.
Mensch, A. and Blondel, M. (2018). Differentiable dynamic programming for structured prediction and attention. In Proc. of ICML.
Papandreou, G. and Yuille, A. L. (2011). Perturb-and-MAP random fields: Using discrete optimization to learn and sample from energy models. In Proc. of ICCV, pages 193-200.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32.
Paulus, M., Choi, D., Tarlow, D., Krause, A., and Maddison, C. J. (2020). Gradient estimation with stochastic softmax tricks. Advances in Neural Information Processing Systems, 33:5691-5704.
Rockafellar, R. T. (1997). Convex analysis, volume 11. Princeton university press.
Sander, M. E., Puigcerver, J., Djolonga, J., Peyre, G., and Blondel, M. (2023). Fast, differentiable and sparse top-k: a convex analysis perspective. arXiv preprint arXiv:2302.01425.
Shvetsova, N., Petersen, F., Kukleva, A., Schiele, B., and Kuehne, H. (2023). Learning by sorting: Self-supervised learning with group ordering constraints. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 16453-16463.
Struminsky, K., Gadetsky, A., Rakitin, D., Karpushkin, D., and Vetrov, D. P. (2021). Leveraging recursive gumbel-max trick for approximate inference in combinatorial spaces. Advances in Neural Information Processing Systems, 34:10999-11011.
Vlastelica, M., Paulus, A., Musil, V., Martius, G., and Rolínek, M. (2019). Differentiation of blackbox combinatorial solvers. arXiv preprint arXiv:1912.02175.
Xie, J., Girshick, R., and Farhadi, A. (2016). Unsupervised deep embedding for clustering analysis. In International conference on machine learning, pages 478-487.
Yang, B., Fu, X., Sidiropoulos, N. D., and Hong, M. (2017). Towards k-means-friendly spaces: Simultaneous deep learning and clustering. In International Conference on Machine Learning, pages 3861-3870. PMLR.
Zmigrod, R., Vieira, T., and Cotterell, R. (2021). Efficient computation of expectations under spanning tree distributions. Transactions of the Association for Computational Linguistics, 9:675-690.