N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs

Autor: Zisman, Ilya, Nikulin, Alexander, Polubarov, Andrei, Lyubaykin, Nikita, Kurenkov, Vladislav

In-context learning allows models like transformers to adapt to new tasks from a few examples without updating their weights, a desirable trait for reinforcement learning (RL). However, existing in-context RL methods, such as Algorithm Distillation (

Externí odkaz: http://arxiv.org/abs/2411.01958

The design, verification, and applications of Hotspice: a Monte Carlo simulator for artificial spin ice

Autor: Maes, Jonathan, De Gusem, Diego, Lateur, Ian, Leliaert, Jonathan, Kurenkov, Aleksandr, Van Waeyenberge, Bartel

We present Hotspice, a Monte Carlo simulation software designed to capture the dynamics and equilibrium states of Artificial Spin Ice (ASI) systems with both in-plane (IP) and out-of-plane (OOP) geometries. An Ising-like model is used where each nano

Externí odkaz: http://arxiv.org/abs/2409.05580

Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

Autor: Aamir, M., Acar, B., Adamov, G., Adams, T., Adloff, C., Afanasiev, S., Agrawal, C., Ahmad, A., Ahmed, H. A., Akbar, S., Akchurin, N., Akgul, B., Akgun, B., Akpinar, R. O., Aktas, E., AlKadhim, A., Alexakhin, V., Alimena, J., Alison, J., Alpana, A., Alshehri, W., Dominguez, P. Alvarez, Alyari, M., Amendola, C., Amir, R. B., Andersen, S. B., Andreev, Y., Antoszczuk, P. D., Aras, U., Ardila, L., Aspell, P., Avila, M., Awad, I., Aydilek, O., Azimi, Z., Pretel, A. Aznar, Bach, O. A., Bainbridge, R., Bakshi, A., Bam, B., Banerjee, S., Barney, D., Bayraktar, O., Beaudette, F., Beaujean, F., Becheva, E., Behera, P. K., Belloni, A., Bergauer, T., Besancon, M., Bylund, O. Bessidskaia, Bhatt, L., Bhowmil, D., Blekman, F., Blinov, P., Bloch, P., Bodek, A., Boger, a., Bonnemaison, A., Bouyjou, F., Brennan, L., Brondolin, E., Brusamolino, A., Bubanja, I., Perraguin, A. Buchot, Bunin, P., Misura, A. Burazin, Butler-nalin, A., Cakir, A., Callier, S., Campbell, S., Canderan, K., Cankocak, K., Cappati, A., Caregari, S., Carron, S., Carty, C., Cauchois, A., Ceard, L., Cerci, S., Chang, P. J., Chatterjee, R. M., Chatterjee, S., Chattopadhyay, P., Chatzistavrou, T., Chaudhary, M. S., Chauhan, A., Chen, J. A., Chen, J., Chen, Y., Cheng, K., Cheung, H., Chhikara, J., Chiron, A., Chiusi, M., Chokheli, D., Chudasama, R., Clement, E., Mendez, S. Coco, Coko, D., Coskun, K., Couderc, F., Crossman, B., Cui, Z., Cuisset, T., Cummings, G., Curtis, E. M., D'Alfonso, M., D-hler-ball, J., Dadazhanova, O., Damgov, J., Das, I., DasGupta, S., Dauncey, P., Mendes, A. David Tinoco, Davies, G., Davignon, O., DeLa, P. deBarbaroC., DeSilva, M., DeWit, A., Debbins, P., Defranchis, M. M., Delagnes, E., Devouge, P., Dewangan, C., DiGuglielmo, G., Diehl, L., Dilsiz, K., Dincer, G. G., Dittmann, J., Dragicevic, M., Du, D., Dubinchik, B., Dugad, S., Dulucq, F., Dumanoglu, I., Duran, B., Dutta, S., Dutta, V., Dychkant, A., Dünser, M., Edberg, T., Ehle, I. T., Berni, A. El, Elias, F., Eno, S. C., Erdogan, E. N., Erkmen, B., Ershov, Y., Ertorer, E. Y., Extier, S., Eychenne, L., Fedar, Y. E., Fedi, G., De Almeida, J. P. Figueiredo De De Sá Sousa, Alves, B. A. Fontana Santos Santos, Frahm, E., Francis, K., Freeman, J., French, T., Gaede, F., Gandhi, P. K., Ganjour, S., Garcia-Bellido, A., Gastaldi, F., Gazi, L., Gecse, Z., Gerwig, H., Gevin, O., Ghosh, S., Gill, K., Gleyzer, S., Godinovic, N., Goek, M., Goettlicher, P., Goff, R., Golunov, A., Gonultas, B., Martínez, J. D. González, Gorbounov, N., Gouskos, L., Gray, A., Gray, L., Grieco, C., Groenroos, S., Groner, D., Gruber, A., Grummer, A., Grönroos, S., Guilloux, F., Guler, Y., Gungordu, A. D., Guo, J., Guo, K., Guler, E. Gurpinar, Gutti, H. K., Guvenli, A. A., Gülmez, E., Hacisahinoglu, B., Halkin, Y., Machado, G. Hamilton Ilha, Hare, H. S., Hatakeyama, K., Heering, A. H., Hegde, V., Heintz, U., Hinton, N., Hinzmann, A., Hirschauer, J., Hitlin, D., Hos, İ., Hou, B., Hou, X., Howard, A., Howe, C., Hsieh, H., Hsu, T., Hua, H., Hummer, F., Imran, M., Incandela, J., Iren, E., Isildak, B., Jackson, P. S., Jackson, W. J., Jain, S., Jana, P., Jaroslavceva, J., Jena, S., Jige, A., Jordano, P. P., Joshi, U., Kaadze, K., Kafizov, A., Kalipoliti, L., Tharayil, A. Kallil, Kaluzinska, O., Kamble, S., Kaminskiy, A., Kanemura, M., Kanso, H., Kao, Y., Kapic, A., Kapsiak, C., Karjavine, V., Karmakar, S., Karneyeu, A., Kaya, M., Topaksu, A. Kayis, Kaynak, B., Kazhykarim, Y., Khan, F. A., Khudiakov, A., Kieseler, J., Kim, R. S., Klijnsma, T., Kloiber, E. G., Klute, M., Kocak, Z., Kodali, K. R., Koetz, K., Kolberg, T., Kolcu, O. B., Komaragiri, J. R., Komm, M., Kopsalis, I., Krause, H. A., Krawczyk, M. A., Vinayakam, T. R. Krishnaswamy, Kristiansen, K., Kristic, A., Krohn, M., Kronheim, B., Krüger, K., Kudtarkar, C., Kulis, S., Kumar, M., Kumar, N., Kumar, S., Verma, R. Kumar, Kunori, S., Kunts, A., Kuo, C., Kurenkov, A., Kuryatkov, V., Kyre, S., Ladenson, J., Lamichhane, K., Landsberg, G., Langford, J., Laudrain, A., Laughlin, R., Lawhorn, J., Dortz, O. Le, Lee, S. W., Lektauers, A., Lelas, D., Leon, M., Levchuk, L., Li, A. J., Li, J., Li, Y., Liang, Z., Liao, H., Lin, K., Lin, W., Lin, Z., Lincoln, D., Linssen, L., Litomin, A., Liu, G., Liu, Y., Lobanov, A., Lohezic, V., Loiseau, T., Lu, C., Lu, R., Lu, S. Y., Lukens, P., Mackenzie, M., Magnan, A., Magniette, F., Mahjoub, A., Mahon, D., Majumder, G., Makarenko, V., Malakhov, A., Malgeri, L., Mallios, S., Mandloi, C., Mankel, A., Mannelli, M., Mans, J., Mantilla, C., Martinez, G., Massa, C., Masterson, P., Matthewman, M., Matveev, V., Mayekar, S., Mazlov, I., Mehta, A., Mestvirishvili, A., Miao, Y., Milella, G., Mirza, I. R., Mitra, P., Moccia, S., Mohanty, G. B., Monti, F., Moortgat, F., Murthy, S., Music, J., Musienko, Y., Nabili, S., Nayak, S., Nelson, J. W., Nema, A., Neutelings, I., Niedziela, J., Nikitenko, A., Noonan, D., Noy, M., Nurdan, K., Obraztsov, S., Ochando, C., Ogul, H., Olsson, J., Onel, Y., Ozkorucuklu, S., Paganis, E., Palit, P., Pan, R., Pandey, S., Pantaleo, F., Papageorgakis, C., Paramesvaran, S., Paranjpe, M. M., Parolia, S., Parsons, A. G., Parygin, P., Paulini, M., Paus, C., Peñaló, K., Pedro, K., Pekic, V., Peltola, T., Peng, B., Perego, A., Perini, D., Petrilli, A., Pham, H., Pierre-Emile, T., Podem, S. K., Popov, V., Portales, L., Potok, O., Pradeep, P. B., Pramanik, R., Prosper, H., Prvan, M., Qasim, S. R., Qu, H., Quast, T., Trivino, A. Quiroga, Rabour, L., Raicevic, N., Rajpoot, H., Rao, M. A., Rapacz, K., Redjeb, W., Reinecke, M., Revering, M., Roberts, A., Rohlf, J., Rosado, P., Rose, A., Rothman, S., Rout, P. 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Z., Terrill, W., Thienpont, D., Thomas, R., Titov, M., Todd, C., Todd, E., Toms, M., Tosun, A., Troska, J., Tsai, L., Tsamalaidze, Z., Tsionou, D., Tsipolitis, G., Tsirigoti, M., Tu, R., Polat, S. N. Tural, Undleeb, S., Usai, E., Uslan, E., Ustinov, V., Vernazza, E., Viahin, O., Viazlo, O., Vichoudis, P., Vijay, A., Virdee, T., Voirin, E., Vojinovic, M., Voytishin, N., Vámi, T. Á., Wade, A., Walter, D., Wang, C., Wang, F., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Wanlin, E., Wayne, M., Wetzel, J., Whitbeck, A., Wickwire, R., Wilmot, D., Wilson, J., Wu, H., Xiao, M., Yang, J., Yazici, B., Ye, Y., Yetkin, T., Yi, R., Yohay, R., Yu, T., Yuan, C., Yuan, X., Yuksel, O., YushmanoV, I., Yusuff, I., Zabi, A., Zareckis, D., Zarubin, A., Zehetner, P., Zghiche, A., Zhang, C., Zhang, D., Zhang, H., Zhang, J., Zhang, Z., Zhao, X., Zhong, J., Zhou, Y., Zorbilmez, Ç.

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensor

Externí odkaz: http://arxiv.org/abs/2406.11937

In-Context Reinforcement Learning for Variable Action Spaces

Autor: Sinii, Viacheslav, Nikulin, Alexander, Kurenkov, Vladislav, Zisman, Ilya, Kolesnikov, Sergey

Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context. A key limitation of previously proposed models is their reliance on a predefined

Externí odkaz: http://arxiv.org/abs/2312.13327

XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX

Autor: Nikulin, Alexander, Kurenkov, Vladislav, Zisman, Ilya, Agarkov, Artem, Sinii, Viacheslav, Kolesnikov, Sergey

Inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid, we present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research. Written in JAX, XLand-MiniGrid is designed t

Externí odkaz: http://arxiv.org/abs/2312.12044