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B. J. Confavreux, “Synapseek: Meta-learning synaptic plasticity rules,” Institute of Science and Technology Austria, 2023.
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H. Fichtenberger, M. H. Henzinger, and J. Upadhyay, “Constant matters: Fine-grained error bound on differentially private continual observation,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 10072–10092.
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A. Shevchenko, K. Kögler, H. Hassani, and M. Mondelli, “Fundamental limits of two-layer autoencoders, and achieving them with gradient methods,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 31151–31209.
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M. Nikdan, T. Pegolotti, E. B. Iofinova, E. Kurtic, and D.-A. Alistarh, “SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 26215–26227.
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T. A. Henzinger, K. Kueffner, and K. Mallik, “Monitoring algorithmic fairness under partial observations,” in 23rd International Conference on Runtime Verification, Thessaloniki, Greece, 2023, vol. 14245, pp. 291–311.
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M. Brooks and R. Seiringer, “The Fröhlich Polaron at strong coupling: Part I - The quantum correction to the classical energy,” Communications in Mathematical Physics, vol. 404. Springer Nature, pp. 287–337, 2023.
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