Zobrazeno 1 - 10
of 12 387
pro vyhledávání: '"Dumoulin, A."'
A Physically Unclonable Function (PUF) is a hardware security primitive used for authentication and key generation. It takes an input bit-vector challenge and produces a single-bit response, resulting in a challenge-response pair (CRP). The truth tab
Externí odkaz:
http://arxiv.org/abs/2406.19975
Autor:
Triantafillou, Eleni, Kairouz, Peter, Pedregosa, Fabian, Hayes, Jamie, Kurmanji, Meghdad, Zhao, Kairan, Dumoulin, Vincent, Junior, Julio Jacques, Mitliagkas, Ioannis, Wan, Jun, Hosoya, Lisheng Sun, Escalera, Sergio, Dziugaite, Gintare Karolina, Triantafillou, Peter, Guyon, Isabelle
We present the findings of the first NeurIPS competition on unlearning, which sought to stimulate the development of novel algorithms and initiate discussions on formal and robust evaluation methodologies. The competition was highly successful: nearl
Externí odkaz:
http://arxiv.org/abs/2406.09073
Autor:
Avignone III, F. T., Barabash, A. S., Berest, V., Bergé, L., Calvo-Mozota, J. M., Carniti, P., Chapellier, M., Dafinei, I., Danevich, F. A., Dumoulin, L., Ferella, F., Ferri, F., Gallas, A., Giuliani, A., Gotti, C., Gras, P., Ianni, A., Imbert, L., Khalife, H., Kobychev, V. V., Konovalov, S. I., Loaiza, P., de Marcillac, P., Marnieros, S., Marrache-Kikuchi, C. A., Martinez, M., Nisi, S., Nones, C., Olivieri, E., de Solórzano, A. Ortiz, Peinaud, Y., Pessina, G., Poda, D. V., Rosier, Ph., Scarpaci, J. A., Tretyak, V. I., Umatov, V. I., Zarytskyy, M. M., Zolotarova, A.
We report on the development of thermal detectors based on large-size tellurium dioxide crystals (45x45x45 mm), containing tellurium enriched in $^{130}$Te to ~91%, for the CROSS double-beta decay experiment. A powder used for the crystals growth was
Externí odkaz:
http://arxiv.org/abs/2406.01444
Autor:
Auguste, D., Barabash, A. S., Berest, V., Bergé, L., Calvo-Mozota, J. M., Carniti, P., Chapellier, M., Dafinei, I., Danevich, F. A., Dixon, T., Dumoulin, L., Ferri, F., Gallas, A., Giuliani, A., Gotti, C., Gras, P., Ianni, A., Imbert, L., Khalife, H., Kobychev, V. V., Konovalov, S. I., Loaiza, P., de Marcillac, P., Marnieros, S., Marrache-Kikuchi, C. A., Martinez, M., Nones, C., Olivieri, E., de Solórzano, A. Ortiz, Peinaud, Y., Pessina, G., Poda, D. V., Rosier, Ph., Scarpaci, J. A., Tretyak, V. I., Umatov, V. I., Zarytskyy, M. M., Zolotarova, A.
The CROSS experiment will search for neutrinoless double-beta decay using a specific mechanical structure to hold thermal detectors. The design of the structure was tuned to minimize the background contribution, keeping an optimal detector performanc
Externí odkaz:
http://arxiv.org/abs/2405.18980
Autor:
Augier, C., Barabash, A. S., Bellini, F., Benato, G., Beretta, M., Bergé, L., Billard, J., Borovlev, Yu. A., Cardani, L., Casali, N., Cazes, A., Celi, E., Chapellier, M., Chiesa, D., Dafinei, I., Danevich, F. A., De Jesus, M., Dixon, T., Dumoulin, L., Eitel, K., Ferri, F., Fujikawa, B. K., Gascon, J., Gironi, L., Giuliani, A., Grigorieva, V. D., Gros, M., Helis, D. L., Huang, H. Z., Huang, R., Imbert, L., Juillard, A., Khalife, H., Kleifges, M., Kobychev, V. V., Kolomensky, Yu. G., Konovalov, S. I., Kotila, J., Loaiza, P., Ma, L., Makarov, E. P., de Marcillac, P., Mariam, R., Marini, L., Marnieros, S., Navick, X. F., Nones, C., Norman, E. B., Olivieri, E., Ouellet, J. L., Pagnanini, L., Pattavina, L., Paul, B., Pavan, M., Peng, H., Pessina, G., Pirro, S., Poda, D. V., Polischuk, O. G., Pozzi, S., Previtali, E., Redon, Th., Rojas, A., Rozov, S., Sanglard, V., Scarpaci, J. A., Schmidt, B., Shen, Y., Shlegel, V. N., Šimkovic, F., Singh, V., Tomei, C., Tretyak, V. I., Umatov, V. I., Vagneron, L., Velázquez, M., War, B., Welliver, B., Winslow, L., Xue, M., Yakushev, E., Zarytskyy, M., Zolotarova, A. S.
The current experiments searching for neutrinoless double-$\beta$ ($0\nu\beta\beta$) decay also collect large statistics of Standard Model allowed two-neutrino double-$\beta$ ($2\nu\beta\beta$) decay events. These can be used to search for Beyond Sta
Externí odkaz:
http://arxiv.org/abs/2405.10766
Autor:
Sepahvand, Nazanin Mohammadi, Dumoulin, Vincent, Triantafillou, Eleni, Dziugaite, Gintare Karolina
As deep learning models are becoming larger and data-hungrier, there are growing ethical, legal and technical concerns over use of data: in practice, agreements on data use may change over time, rendering previously-used training data impermissible f
Externí odkaz:
http://arxiv.org/abs/2405.10425
Autor:
Williams, Ben, van Merriënboer, Bart, Dumoulin, Vincent, Hamer, Jenny, Triantafillou, Eleni, Fleishman, Abram B., McKown, Matthew, Munger, Jill E., Rice, Aaron N., Lillis, Ashlee, White, Clemency E., Hobbs, Catherine A. D., Razak, Tries B., Jones, Kate E., Denton, Tom
Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments. However, high annotation and compute costs limit the field's efficacy. Generalizable pretrained networks can overcome these costs, but h
Externí odkaz:
http://arxiv.org/abs/2404.16436
Autor:
Armatol, A., Augier, C., Bandac, I. C., Baudin, D., Benato, G., Berest, V., Bergé, L., Billard, J., Calvo-Mozota, J. M., Carniti, P., Chapellier, M., Danevich, F. A., De Jesus, M., Dixon, T., Dumoulin, L., Ferri, F., Gascon, J., Giuliani, A., Gomez, H., Gotti, C., Gras, Ph., Gros, M., Juillard, A., Khalife, H., Kobychev, V. V., Lefevre, M., Loaiza, P., de Marcillac, P., Marnieros, S., Marrache-Kikuchi, C. A., Martinez, M., Mas, Ph., Mazzucato, E., Millot, J. F., Nones, C., Olivieri, E., de Solórzano, A. Ortiz, Pessina, G., Poda, D. V., Rojas, A., Scarpaci, J. A., Schmidt, B., Tellier, O., Tretyak, V. I., Warot, G., Zampieri, Th., Zarytskyy, M. M., Zolotarova, A.
BINGO is a project aiming to set the grounds for large-scale bolometric neutrinoless double-beta-decay experiments capable of investigating the effective Majorana neutrino mass at a few meV level. It focuses on developing innovative technologies to a
Externí odkaz:
http://arxiv.org/abs/2402.12262
Autor:
Hamer, Jenny, Triantafillou, Eleni, van Merriënboer, Bart, Kahl, Stefan, Klinck, Holger, Denton, Tom, Dumoulin, Vincent
The ability for a machine learning model to cope with differences in training and deployment conditions--e.g. in the presence of distribution shift or the generalization to new classes altogether--is crucial for real-world use cases. However, most em
Externí odkaz:
http://arxiv.org/abs/2312.07439
Learning from human feedback (LHF) -- and in particular learning from pairwise preferences -- has recently become a crucial ingredient in training large language models (LLMs), and has been the subject of much research. Most recent works frame it as
Externí odkaz:
http://arxiv.org/abs/2311.14115