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pro vyhledávání: '"Titov AS"'
Neural speech models build deeply entangled internal representations, which capture a variety of features (e.g., fundamental frequency, loudness, syntactic category, or semantic content of a word) in a distributed encoding. This complexity makes it d
Externí odkaz:
http://arxiv.org/abs/2410.03037
Autor:
From, Jorge, Hernández-Bernal, Jorge, Moinelo, Alejandro Cardesin, Hueso, Ricardo, Ravanis, Eleni, Sierra, Abel Burgos, Wood, Simon, Sitja, Marc Costa, Escalante, Alfredo, Grotheer, Emmanuel, de la Parra, Julia Marin Yaseli, Merrit, Donald, Almeida, Miguel, Breitfellner, Michel, Sierra, Mar, Martin, Patrick, Titov, Dmitri, Wilson, Colin, Larsen, Ethan, Gaztelurrutia, Teresa del Rio, Lavega, Agustin Sanchez
The Visual Monitoring Camera (VMC) is a small imaging instrument onboard Mars Express with a field of view of ~40x30 degrees. The camera was initially intended to provide visual confirmation of the separation of the Beagle 2 lander and has similar te
Externí odkaz:
http://arxiv.org/abs/2410.02999
Large language models (LLMs) have demonstrated impressive few-shot in-context learning (ICL) abilities. Still, we show that they are sometimes prone to a `copying bias', where they copy answers from provided examples instead of learning the underlyin
Externí odkaz:
http://arxiv.org/abs/2410.01288
We generalize a magnetogram-matching Biot-Savart law (BSL) from planar to spherical geometry. For a given coronal current density $\bf{J}$, this law determines the corresponding magnetic field $\tilde{\bf B}$ under the condition that its radial compo
Externí odkaz:
http://arxiv.org/abs/2410.02134
Reinforcement Learning from Human Feedback aligns the outputs of Large Language Models with human values and preferences. Central to this process is the reward model (RM), which translates human feedback into training signals for optimising LLM behav
Externí odkaz:
http://arxiv.org/abs/2409.17407
Autor:
Xu, Shuangjing, Jung, Taehyun, Zhang, Bo, Xu, Ming Hui, Byun, Do-Young, He, Xuan, Sakai, Nobuyuki, Titov, Oleg, Shu, Fengchun, Kim, Hyo-Ryoung, Cho, Jungho, Yoo, Sung-Moon, Choi, Byung-Kyu, Lee, Woo Kyoung, Sun, Yan, Mai, Xiaofeng, Wang, Guangli
Extending geodetic and astrometric Very Long Baseline Interferometry (VLBI) observations from traditional centimeter wavebands to millimeter wavebands offers numerous scientific potentials and benefits. However, it was considered quite challenging du
Externí odkaz:
http://arxiv.org/abs/2409.07309
Despite recent advances in large-scale text-to-image generative models, manipulating real images with these models remains a challenging problem. The main limitations of existing editing methods are that they either fail to perform with consistent qu
Externí odkaz:
http://arxiv.org/abs/2409.01322
The scaling of large language models (LLMs) has revolutionized their capabilities in various tasks, yet this growth must be matched with efficient computational strategies. The Mixture-of-Experts (MoE) architecture stands out for its ability to scale
Externí odkaz:
http://arxiv.org/abs/2408.06793
Autor:
Aker, M., Batzler, D., Beglarian, A., Behrens, J., Beisenkötter, J., Biassoni, M., Bieringer, B., Biondi, Y., Block, F., Bobien, S., Böttcher, M., Bornschein, B., Bornschein, L., Caldwell, T. S., Carminati, M., Chatrabhuti, A., Chilingaryan, S., Daniel, B. A., Debowski, K., Descher, M., Barrero, D. Díaz, Doe, P. J., Dragoun, O., Drexlin, G., Edzards, F., Eitel, K., Ellinger, E., Engel, R., Enomoto, S., Felden, A., Fengler, C., Fiorini, C., Formaggio, J. A., Forstner, C., Fränkle, F. M., Gauda, K., Gavin, A. S., Gil, W., Glück, F., Grössle, R., Gumbsheimer, R., Hannen, V., Hasselmann, L., Haußmann, N., Helbing, K., Heyns, S., Hickford, S., Hiller, R., Hillesheimer, D., Hinz, D., Höhn, T., Huber, A., Jansen, A., Karl, C., Kellerer, J., Khosonthongkee, K., Köhler, C., Köllenberger, L., Kopmann, A., Kovač, N., Krause, H., La Cascio, L., Lasserre, T., Lauer, J., Le, T. L., Lebeda, O., Lehnert, B., Li, G., Lokhov, A., Machatschek, M., Mark, M., Marsteller, A., Martin, E. L., McMichael, K., Melzer, C., Mertens, S., Mohanty, S., Mostafa, J., Müller, K., Nava, A., Neumann, H., Niemes, S., Parno, D. S., Pavan, M., Pinsook, U., Poon, A. W. P., Poyato, J. M. L., Pozzi, S., Priester, F., Ráliš, J., Ramachandran, S., Robertson, R. G. H., Rodenbeck, C., Röllig, M., Sack, R., Saenz, A., Salomon, R., Schäfer, P., Schlösser, M., Schlösser, K., Schlüter, L., Schneidewind, S., Schrank, M., Schürmann, J., Schütz, A. K., Schwemmer, A., Schwenck, A., Šefčík, M., Siegmann, D., Simon, F., Spanier, F., Spreng, D., Sreethawong, W., Steidl, M., Štorek, J., Stribl, X., Sturm, M., Suwonjandee, N., Jerome, N. Tan, Telle, H. H., Thorne, L. A., Thümmler, T., Titov, N., Tkachev, I., Urban, K., Valerius, K., Vénos, D., Weinheimer, C., Welte, S., Wendel, J., Wiesinger, C., Wilkerson, J. F., Wolf, J., Wüstling, S., Wydra, J., Xu, W., Zadorozhny, S., Zeller, G.
The projected sensitivity of the effective electron neutrino-mass measurement with the KATRIN experiment is below 0.3 eV (90 % CL) after five years of data acquisition. The sensitivity is affected by the increased rate of the background electrons fro
Externí odkaz:
http://arxiv.org/abs/2408.07022
Autor:
Dankers, Verna, Titov, Ivan
Memorisation is a natural part of learning from real-world data: neural models pick up on atypical input-output combinations and store those training examples in their parameter space. That this happens is well-known, but how and where are questions
Externí odkaz:
http://arxiv.org/abs/2408.04965