Machine learning enables completely automatic tuning of a quantum device faster than human experts by Moon, H., Lennon, D. T., Kirkpatrick, J., van Esbroeck, N. M., Camenzind, L. C., Yu, Liuqi, Vigneau, F., Zumbühl, D. M., Briggs, G. A. D., Osborne, M. A., Sejdinovic, D., Laird, E. A., Ares, N.

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Quantum device fine-tuning using unsupervised embedding learning by van Esbroeck, N M, Lennon, D T, Moon, H, Nguyen, V, Vigneau, F, Camenzind, L C, Yu, L, Zumbühl, D M, Briggs, G A D, Sejdinovic, D, Ares, N

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Expanding window fountain codes for unequal error protection by Sejdinovic, D., Vukobratovic, D., Doufexi, A., Senk, V., Piechocki, R.

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Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning by Severin, B., Lennon, D. T., Camenzind, L. C., Vigneau, F., Fedele, F., Jirovec, D., Ballabio, A., Chrastina, D., Isella, G., de Kruijf, M., Carballido, M. J., Svab, S., Kuhlmann, A. V., Geyer, S., Froning, F. N. M., Moon, H., Osborne, M. A., Sejdinovic, D., Katsaros, G., Zumbühl, D. M., Briggs, G. A. D., Ares, N.

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Deep reinforcement learning for efficient measurement of quantum devices by Nguyen, V., Orbell, S. B., Lennon, D. T., Moon, H., Vigneau, F., Camenzind, L. C., Yu, L., Zumbühl, D. M., Briggs, G. A. D., Osborne, M. A., Sejdinovic, D., Ares, N.

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Discussion of ‘Multi-scale Fisher’s independence test for multivariate dependence by Schrab, A, Jitkrittum, W, Szabó, Z, Sejdinovic, D, Gretton, A

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Scalable Video Multicast Using Expanding Window Fountain Codes by Vukobratovic, D., Stankovic, V., Sejdinovic, D., Stankovic, L., Zixiang Xiong

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Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning by Craig, D. L., Moon, H., Fedele, F., Lennon, D. T., van Straaten, B., Vigneau, F., Camenzind, L. C., Zumbühl, D. M., Briggs, G. A. D., Osborne, M. A., Sejdinovic, D., Ares, N.

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Fountain code design for data multicast with side information by Sejdinovic, D., Piechocki, R., Doufexi, A., Ismail, M.

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Marine cloud base height retrieval from MODIS cloud properties using machine learning by Lenhardt, Julien, Quaas, Johannes, Sejdinovic, Dino

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Decentralised distributed fountain coding: asymptotic analysis and design by Sejdinovic, D., Piechocki, R., Doufexi, A., Ismail, M.

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Combinatorial channel signature modulation for wireless ad-hoc networks by Piechocki, R. J., Sejdinovic, D.

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Approximate message passing under finite alphabet constraints by Muller, A., Sejdinovic, D., Piechocki, R.

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Expanding Window Fountain Codes for Unequal Error Protection by Sejdinovic, D., Vukobratovic, D., Doufexi, A., Senk, V., Piechocki, R.J.

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The Throughput Analysis of Different IR-HARQ Schemes Based on Fountain Codes by Sejdinovic, D., Ponnampalam, V., Piechocki, R.J., Doufexi, A.

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Fountain Coding with Decoder Side Information by Sejdinovic, D., Piechocki, R.J., Doufexi, A., Ismail, M.

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Note on noisy group testing: Asymptotic bounds and belief propagation reconstruction by Sejdinovic, D, Johnson, O

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AND-OR tree analysis of distributed LT codes by Sejdinovic, D., Piechocki, R.J., Doufexi, A.

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Deep Reinforcement Learning for Efficient Measurement of Quantum Devices by Nguyen, V, Orbell, S B, Lennon, D T, Moon, H, Vigneau, F, Camenzind, L C, L Yu, Zumbühl, D M, Briggs, G A D, Osborne, M A, Sejdinovic, D, Ares, N

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Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning by Severin, B, Lennon, D T, Camenzind, L C, Vigneau, F, Fedele, F, Jirovec, D, Ballabio, A, Chrastina, D, Isella, G, de Kruijf, M, Carballido, M J, Svab, S, Kuhlmann, A V, Braakman, F R, Geyer, S, Froning, F N M, Moon, H, Osborne, M A, Sejdinovic, D, Katsaros, G, Zumbühl, D M, Briggs, G A D, Ares, N

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