Zobrazeno 1 - 10
of 6 690
pro vyhledávání: '"A. A Zaytsev"'
Change point detection (CPD) methods aim to identify abrupt shifts in the distribution of input data streams. Accurate estimators for this task are crucial across various real-world scenarios. Yet, traditional unsupervised CPD techniques face signifi
Externí odkaz:
http://arxiv.org/abs/2410.13637
Collusion is a complex phenomenon in which companies secretly collaborate to engage in fraudulent practices. This paper presents an innovative methodology for detecting and predicting collusion patterns in different national markets using neural netw
Externí odkaz:
http://arxiv.org/abs/2410.07091
We give new formulas for reconstructions from band-limited Hankel transform of integer or half-integer order. Our formulas rely on the PSWF-Radon approach to super-resolution in multidimensional Fourier analysis. This approach consists of combining t
Externí odkaz:
http://arxiv.org/abs/2409.17409
Autor:
Erlygin, Leonid, Zaytsev, Alexey
Accurately estimating image quality and model robustness improvement are critical challenges in unconstrained face recognition, which can be addressed through uncertainty estimation via probabilistic face embeddings. Previous research mainly focused
Externí odkaz:
http://arxiv.org/abs/2408.14229
Autor:
Yugay, Aleksandr, Zaytsev, Alexey
High-quality representation of transactional sequences is vital for modern banking applications, including risk management, churn prediction, and personalized customer offers. Different tasks require distinct representation properties: local tasks be
Externí odkaz:
http://arxiv.org/abs/2408.09995
Autor:
Kuleshov, Ilya, Boeva, Galina, Zhuzhel, Vladislav, Romanenkova, Evgenia, Vorsin, Evgeni, Zaytsev, Alexey
Observation of the underlying actors that generate event sequences reveals that they often evolve continuously. Most modern methods, however, tend to model such processes through at most piecewise-continuous trajectories. To address this, we adopt a
Externí odkaz:
http://arxiv.org/abs/2408.08055
Knowledge Graph Embedding (KGE) is a popular approach, which aims to represent entities and relations of a knowledge graph in latent spaces. Their representations are known as embeddings. To measure the plausibility of triplets, score functions are d
Externí odkaz:
http://arxiv.org/abs/2407.16326
Autor:
SuperCDMS Collaboration, Albakry, M. F., Alkhatib, I., Alonso-González, D., Amaral, D. W. P., Anczarski, J., Aralis, T., Aramaki, T., Arnquist, I. J., Langroudy, I. Ataee, Azadbakht, E., Bathurst, C., Bhattacharyya, R., Biffl, A. J., Brink, P. L., Buchanan, M., Bunker, R., Cabrera, B., Calkins, R., Cameron, R. A., Cartaro, C., Cerdeño, D. G., Chang, Y. -Y., Chaudhuri, M., Chen, J. -H., Chen, R., Chott, N., Cooley, J., Coombes, H., Cushman, P., Cyna, R., Das, S., De Brienne, F., Dharani, S., di Vacri, M. L., Diamond, M. D., Elwan, M., Fascione, E., Figueroa-Feliciano, E., Fouts, K., Fritts, M., Germond, R., Ghaith, M., Golwala, S. R., Hall, J., Harms, S. A. S., Harris, K., Hassan, N., Hong, Z., Hoppe, E. W., Hsu, L., Huber, M. E., Iyer, V., Jardin, D., Kashyap, V. K. S., Keller, S. T. D., Kelsey, M. H., Kennard, K. T., Kubik, A., Kurinsky, N. A., Lee, M., Leyva, J., Liu, J., Liu, Y., Loer, B., Asamar, E. Lopez, Lukens, P., MacFarlane, D. B., Mahapatra, R., Mammo, J. S., Mast, N., Mayer, A. J., Theenhausen, H. Meyer zu, Michaud, É., Michielin, E., Mirabolfathi, N., Mirzakhani, M., Mohanty, B., Monteiro, D., Nelson, J., Neog, H., Novati, V., Orrell, J. L., Osborne, M. D., Oser, S. M., Pandey, L., Pandey, S., Partridge, R., Pedreros, D. S., Peng, W., Perna, L., Perry, W. L., Podviianiuk, R., Poudel, S. S., Pradeep, A., Pyle, M., Rau, W., Reid, E., Ren, R., Reynolds, T., Rios, M., Roberts, A., Robinson, A. E., Ryan, J. L., Saab, T., Sadek, D., Sadoulet, B., Sahoo, S. P., Saikia, I., Sander, J., Sattari, A., Schmidt, B., Schnee, R. W., Scorza, S., Serfass, B., Simchony, A., Sincavage, D. J., Sinervo, P., Street, J., Sun, H., Tanner, E., Terry, G. D., Toback, D., Verma, S., Villano, A. N., von Krosigk, B., Watkins, S. L., Wen, O., Williams, Z., Wilson, M. J., Winchell, J., Wykoff, K., Yellin, S., Young, B. A., Yu, T. C., Zatschler, B., Zatschler, S., Zaytsev, A., Zhang, E., Zheng, L., Zuniga, A., Zurowski, M. J.
This article presents constraints on dark-matter-electron interactions obtained from the first underground data-taking campaign with multiple SuperCDMS HVeV detectors operated in the same housing. An exposure of 7.63 g-days is used to set upper limit
Externí odkaz:
http://arxiv.org/abs/2407.08085
This paper presents a solution for the Multilingual Text Detoxification task in the PAN-2024 competition of the SmurfCat team. Using data augmentation through machine translation and a special filtering procedure, we collected an additional multiling
Externí odkaz:
http://arxiv.org/abs/2407.05449
Autor:
Fumagalli, Mattia, Sales, Tiago Prince, Barcelos, Pedro Paulo F., Micale, Giovanni, Zaytsev, Vadim, Calvanese, Diego, Guizzardi, Giancarlo
The problem of using structured methods to represent knowledge is well-known in conceptual modeling and has been studied for many years. It has been proven that adopting modeling patterns represents an effective structural method. Patterns are, indee
Externí odkaz:
http://arxiv.org/abs/2406.07129