Zobrazeno 1 - 10
of 5 810
pro vyhledávání: '"Solin, A."'
Autor:
Scannell, Aidan, Kujanpää, Kalle, Zhao, Yi, Nakhaei, Mohammadreza, Solin, Arno, Pajarinen, Joni
Learning representations for reinforcement learning (RL) has shown much promise for continuous control. We propose an efficient representation learning method using only a self-supervised latent-state consistency loss. Our approach employs an encoder
Externí odkaz:
http://arxiv.org/abs/2406.02696
Autor:
Tamir, Ella, Solin, Arno
Learning dynamical systems from sparse observations is critical in numerous fields, including biology, finance, and physics. Even if tackling such problems is standard in general information fusion, it remains challenging for contemporary machine lea
Externí odkaz:
http://arxiv.org/abs/2406.00561
In the domains of image and audio, diffusion models have shown impressive performance. However, their application to discrete data types, such as language, has often been suboptimal compared to autoregressive generative models. This paper tackles the
Externí odkaz:
http://arxiv.org/abs/2405.17889
Retrosynthesis, the task of identifying precursors for a given molecule, can be naturally framed as a conditional graph generation task. Diffusion models are a particularly promising modelling approach, enabling post-hoc conditioning and trading off
Externí odkaz:
http://arxiv.org/abs/2405.17656
Autor:
The SPD Collaboration, Abazov, V., Abramov, V., Afanasyev, L., Akhunzyanov, R., Akindinov, A., Alekseev, I., Aleshko, A., Alexakhin, V., Alexeev, G., Alimov, L., Allakhverdieva, A., Amoroso, A., Andreev, V., Andronov, E., Anikin, Yu., Anischenko, S., Anisenkov, A., Anosov, V., Antokhin, E., Antonov, A., Antsupov, S., Anufriev, A., Asadova, K., Ashraf, S., Astakhov, V., Aynikeev, A., Azarkin, M., Azorskiy, N., Bagulya, A., Baigarashev, D., Baldin, A., Baldina, E., Barbashina, N., Barnyakov, A., Barsov, S., Bartkevich, A., Baryshevsky, V., Basharina, K., Baskakov, A., Baskov, V., Batista, M., Baturitsky, M., Bautin, V., Bedareva, T., Belokurova, S., Belova, A., Belyaeva, E., Berdnikov, A., Berdnikov, Ya., Berezhnoy, A., Berngardt, A., Bespalov, Yu., Bleko, V., Bliznyuk, L., Bogoslovskii, D., Boiko, A., Boikov, A., Bolsunovskya, M., Boos, E., Borisov, V., Borsch, V., Budkouski, D., Bulanova, S., Bulekov, O., Bunichev, V., Burtebayev, N., Bychanok, D., Casanova, A., Cesar, G., Chemezov, D., Chepurnov, A., Chen, L., Chmill, V., Chukanov, A., Chuzo, A., Danilyuk, A., Datta, A., Dedovich, D., Demichev, M., Deng, G., Denisenko, I., Denisov, O., Derbysheva, T., Derkach, D., Didorenko, A., Dima, M. -O., Doinikov, A., Doronin, S., Dronik, V., Dubinin, F., Dunin, V., Durum, A., Egorov, A., El-Kholy, R., Enik, T., Ermak, D., Erofeev, D., Erokhin, A., Ezhov, D., Fedin, O., Fedotova, Ju., Feofilov, G., Filatov, Yu., Filimonov, S., Frolov, V., Galaktionov, K., Galoyan, A., Garkun, A., Gavrishchuk, O., Gerasimov, S., Gerassimov, S., Gilts, M., Gladilin, L., Golovanov, G., Golovnya, S., Golovtsov, V., Golubev, A., Golubykh, S., Goncharov, P., Gongadze, A., Greben, N., Gregoryev, A., Gribkov, D., Gridin, A., Gritsay, K., Gubachev, D., Guo, J., Gurchin, Yu., Gurinovich, A., Gurov, Yu., Guskov, A., Gutierrez, D., Guzman, F., Hakobyan, A., Han, D., Harkusha, S., Hu, Sh., Igolkin, S., Isupov, A., Ivanov, A., Ivanov, N., Ivantchenko, V., Jin, Sh., Kakurin, S., Kalinichenko, N., Kambar, Y., Kantsyrev, A., Kapitonov, I., Karjavine, V., Karpishkov, A., Katcin, A., Kekelidze, G., Kereibay, D., Khabarov, S., Kharyuzov, P., Khodzhibagiyan, H., Kidanov, E., Kidanova, E., Kim, V., Kiryanov, A., Kishchin, I., Kokoulina, E., Kolbasin, A., Komarov, V., Konak, A., Kopylov, Yu., Korjik, M., Korotkov, M., Korovkin, D., Korzenev, A., Kostenko, B., Kotova, A., Kotzinian, A., Kovalenko, V., Kovyazina, N., Kozhin, M., Kraeva, A., Kramarenko, V., Kremnev, A., Kruchonak, U., Kubankin, A., Kuchinskaia, O., Kulchitsky, Yu., Kuleshov, S., Kulikov, A., Kulikov, V., Kurbatov, V., Kurmanaliev, Zh., Kurochkin, Yu., Kutuzov, S., Kuznetsova, E., Kuyanov, I., Ladygin, E., Ladygin, V., Larionova, D., Lebedev, V., Levchuk, M., Li, P., Li, X., Li, Y., Livanov, A., Lednicki, R., Lobanov, A., Lobko, A., Loshmanova, K., Lukashevich, S., Luschevskaya, E., Lyashko, A. L'vov I., Lysan, V., Lyubovitskij, V., Madigozhin, D., Makarenko, V., Makarov, N., Makhmanazarov, R., Maleev, V., Maletic, D., Malinin, A., Maltsev, A., Maltsev, N., Malkhasyan, A., Malyshev, M., Mamoutova, O., Manakonov, A., Marova, A., Merkin, M., Meshkov, I., Metchinsky, V., Minko, O., Mitrankov, Yu., Mitrankova, M., Mkrtchyan, A., Mkrtchyan, H., Mohamed, R., Morozova, S., Morozikhin, A., Mosolova, E., Mossolov, V., Movchan, S., Mukhamejanov, Y., Mukhamejanova, A., Muzyaev, E., Myktybekov, D., Nagorniy, S., Nassurlla, M., Nechaeva, P., Negodaev, M., Nesterov, V., Nevmerzhitsky, M., Nigmatkulov, G., Nikiforov, D., Nikitin, V., Nikolaev, A., Oleynik, D., Onuchin, V., Orlov, I., Orlova, A., Ososkov, G., Panzieri, D., Parsamyan, B., Pavzderin, P., Pavlov, V., Pedraza, M., Perelygin, V., Peshkov, D., Petrosyan, A., Petrov, M., Petrov, V., Petrukhin, K., Piskun, A., Pivovarov, S., Polishchuk, I., Polozov, P., Polyanskii, V., Ponomarev, A., Popov, V., Popovich, S., Prokhorova, D., Prokofiev, N., Prokoshin, F., Puchkov, A., Pudin, I., Pyata, E., Ratnikov, F., Rasin, V., Red'kov, V., Reshetin, A., Reznikov, S., Rogacheva, N., Romakhov, S., Rouba, A., Rudnev, V., Rusinov, V., Rusov, D., Ryltsov, V., Saduyev, N., Safonov, A., Sakhiyev, S., Salamatin, K., Saleev, V., Samartsev, A., Samigullin, E., Samoylov, O., Saprunov, E., Savenkov, A., Seleznev, A., Semak, A., Senkov, D., Sergeev, A., Seryogin, L., Seryubin, S., Shabanov, A., Shahinyan, A., Shavrin, A., Shein, I., Sheremeteva, A., Shevchenko, V., Shilyaev, K., Shimansky, S., Shinbulatov, S., Shipilov, F., Shipilova, A., Shkarovskiy, S., Shoukovy, D., Shpakov, K., Shreyber, I., Shtejer, K., Shulyakovsky, R., Shunko, A., Sinelshchikova, S., Skachkova, A., Skalnenkov, A., Smirnov, A., Smirnov, S., Snesarev, A., Solin, A., Solin jr., A., Soldatov, E., Solovtsov, V., Song, J., Sosnov, D., Stavinskiy, A., Stekacheva, D., Streletskaya, E., Strikhanov, M., Suarez, O., Sukhikh, A., Sukhovarov, S., Sulin, V., Sultanov, R., Sun, P., Svirida, D., Syresin, E., Tadevosyan, V., Tarasov, O., Tarkovsky, E., Tchekhovsky, V., Tcherniaev, E., Terekhin, A., Terkulov, A., Tereshchenko, V., Teryaev, O., Teterin, P., Tishevsky, A., Tokmenin, V., Topilin, N., Tsiareshka, P., Tumasyan, A., Tyumenkov, G., Usenko, E., Uvarov, L., Uzhinsky, V., Uzikov, Yu., Valiev, F., Vasilieva, E., Vasyukov, A., Vechernin, V., Verkheev, A., Vertogradov, L., Vertogradova, Yu., Vidal, R., Voitishin, N., Volkov, I., Volkov, P., Vorobyov, A., Voskanyan, H., Wang, H., Wang, Y., Xu, T., Yanovich, A., Yeletskikh, I., Yerezhep, N., Yurchenko, S., Zakharov, A., Zamiatin, N., Zamora-Saá, J., Zarochentsev, A., Zelenov, A., Zemlyanichkina, E., Zhabitsky, M., Zhang, J., Zhang, Zh., Zhemchugov, A., Zherebchevsky, V., Zhevlakov, A., Zhigareva, N., Zhou, J., Zhuang, X., Zhukov, I., Zhuravlev, N., Zinin, A., Zmeev, S., Zolotykh, D., Zubarev, E., Zvyagina, A.
The Spin Physics Detector collaboration proposes to install a universal detector in the second interaction point of the NICA collider under construction (JINR, Dubna) to study the spin structure of the proton and deuteron and other spin-related pheno
Externí odkaz:
http://arxiv.org/abs/2404.08317
Deployment of deep neural networks in real-world settings typically requires adaptation to new tasks with few examples. Few-shot classification (FSC) provides a solution to this problem by leveraging pre-trained backbones for fast adaptation to new c
Externí odkaz:
http://arxiv.org/abs/2404.07696
Autor:
Seiskari, Otto, Ylilammi, Jerry, Kaatrasalo, Valtteri, Rantalankila, Pekka, Turkulainen, Matias, Kannala, Juho, Solin, Arno
High-quality scene reconstruction and novel view synthesis based on Gaussian Splatting (3DGS) typically require steady, high-quality photographs, often impractical to capture with handheld cameras. We present a method that adapts to camera motion and
Externí odkaz:
http://arxiv.org/abs/2403.13327
Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining prior knowledge. While Gaussian processes elegantly tackle these problems, they struggle with scalability and hand
Externí odkaz:
http://arxiv.org/abs/2403.10929
Autor:
Loconte, Lorenzo, Sladek, Aleksanteri M., Mengel, Stefan, Trapp, Martin, Solin, Arno, Gillis, Nicolas, Vergari, Antonio
Mixture models are traditionally represented and learned by adding several distributions as components. Allowing mixtures to subtract probability mass or density can drastically reduce the number of components needed to model complex distributions. H
Externí odkaz:
http://arxiv.org/abs/2310.00724
Autor:
Yu, Xuanlong, Zuo, Yi, Wang, Zitao, Zhang, Xiaowen, Zhao, Jiaxuan, Yang, Yuting, Jiao, Licheng, Peng, Rui, Wang, Xinyi, Zhang, Junpei, Zhang, Kexin, Liu, Fang, Alcover-Couso, Roberto, SanMiguel, Juan C., Escudero-Viñolo, Marcos, Tian, Hanlin, Matsui, Kenta, Wang, Tianhao, Adan, Fahmy, Gao, Zhitong, He, Xuming, Bouniot, Quentin, Moghaddam, Hossein, Rai, Shyam Nandan, Cermelli, Fabio, Masone, Carlo, Pilzer, Andrea, Ricci, Elisa, Bursuc, Andrei, Solin, Arno, Trapp, Martin, Li, Rui, Yao, Angela, Chen, Wenlong, Simpson, Ivor, Campbell, Neill D. F., Franchi, Gianni
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segmentation in urban environments, with a particular focus on natural advers
Externí odkaz:
http://arxiv.org/abs/2309.15478