Zobrazeno 1 - 10
of 25 858
pro vyhledávání: '"A Sessa"'
Autor:
Alviggi, M., Biglietti, M., Camerlingo, M. T., Della Pietra, M., Di Donato, C., Di Nardo, R., Franchellucci, S., Iengo, P., Iodice, M., Petrucci, F., Sekhniaidze, G., Sessa, M.
The aim of the presented work is the development of single-stage amplification resistive Micro Pattern Gas Detectors (MPGD) based on Micromegas technology with the following characteristics: ability to efficiently operate up to 10 MHz/cm$^2$ counting
Externí odkaz:
http://arxiv.org/abs/2411.17202
Bayesian optimization (BO) is a powerful framework to optimize black-box expensive-to-evaluate functions via sequential interactions. In several important problems (e.g. drug discovery, circuit design, neural architecture search, etc.), though, such
Externí odkaz:
http://arxiv.org/abs/2409.18582
Autor:
Gemma Team, Riviere, Morgane, Pathak, Shreya, Sessa, Pier Giuseppe, Hardin, Cassidy, Bhupatiraju, Surya, Hussenot, Léonard, Mesnard, Thomas, Shahriari, Bobak, Ramé, Alexandre, Ferret, Johan, Liu, Peter, Tafti, Pouya, Friesen, Abe, Casbon, Michelle, Ramos, Sabela, Kumar, Ravin, Lan, Charline Le, Jerome, Sammy, Tsitsulin, Anton, Vieillard, Nino, Stanczyk, Piotr, Girgin, Sertan, Momchev, Nikola, Hoffman, Matt, Thakoor, Shantanu, Grill, Jean-Bastien, Neyshabur, Behnam, Bachem, Olivier, Walton, Alanna, Severyn, Aliaksei, Parrish, Alicia, Ahmad, Aliya, Hutchison, Allen, Abdagic, Alvin, Carl, Amanda, Shen, Amy, Brock, Andy, Coenen, Andy, Laforge, Anthony, Paterson, Antonia, Bastian, Ben, Piot, Bilal, Wu, Bo, Royal, Brandon, Chen, Charlie, Kumar, Chintu, Perry, Chris, Welty, Chris, Choquette-Choo, Christopher A., Sinopalnikov, Danila, Weinberger, David, Vijaykumar, Dimple, Rogozińska, Dominika, Herbison, Dustin, Bandy, Elisa, Wang, Emma, Noland, Eric, Moreira, Erica, Senter, Evan, Eltyshev, Evgenii, Visin, Francesco, Rasskin, Gabriel, Wei, Gary, Cameron, Glenn, Martins, Gus, Hashemi, Hadi, Klimczak-Plucińska, Hanna, Batra, Harleen, Dhand, Harsh, Nardini, Ivan, Mein, Jacinda, Zhou, Jack, Svensson, James, Stanway, Jeff, Chan, Jetha, Zhou, Jin Peng, Carrasqueira, Joana, Iljazi, Joana, Becker, Jocelyn, Fernandez, Joe, van Amersfoort, Joost, Gordon, Josh, Lipschultz, Josh, Newlan, Josh, Ji, Ju-yeong, Mohamed, Kareem, Badola, Kartikeya, Black, Kat, Millican, Katie, McDonell, Keelin, Nguyen, Kelvin, Sodhia, Kiranbir, Greene, Kish, Sjoesund, Lars Lowe, Usui, Lauren, Sifre, Laurent, Heuermann, Lena, Lago, Leticia, McNealus, Lilly, Soares, Livio Baldini, Kilpatrick, Logan, Dixon, Lucas, Martins, Luciano, Reid, Machel, Singh, Manvinder, Iverson, Mark, Görner, Martin, Velloso, Mat, Wirth, Mateo, Davidow, Matt, Miller, Matt, Rahtz, Matthew, Watson, Matthew, Risdal, Meg, Kazemi, Mehran, Moynihan, Michael, Zhang, Ming, Kahng, Minsuk, Park, Minwoo, Rahman, Mofi, Khatwani, Mohit, Dao, Natalie, Bardoliwalla, Nenshad, Devanathan, Nesh, Dumai, Neta, Chauhan, Nilay, Wahltinez, Oscar, Botarda, Pankil, Barnes, Parker, Barham, Paul, Michel, Paul, Jin, Pengchong, Georgiev, Petko, Culliton, Phil, Kuppala, Pradeep, Comanescu, Ramona, Merhej, Ramona, Jana, Reena, Rokni, Reza Ardeshir, Agarwal, Rishabh, Mullins, Ryan, Saadat, Samaneh, Carthy, Sara Mc, Cogan, Sarah, Perrin, Sarah, Arnold, Sébastien M. R., Krause, Sebastian, Dai, Shengyang, Garg, Shruti, Sheth, Shruti, Ronstrom, Sue, Chan, Susan, Jordan, Timothy, Yu, Ting, Eccles, Tom, Hennigan, Tom, Kocisky, Tomas, Doshi, Tulsee, Jain, Vihan, Yadav, Vikas, Meshram, Vilobh, Dharmadhikari, Vishal, Barkley, Warren, Wei, Wei, Ye, Wenming, Han, Woohyun, Kwon, Woosuk, Xu, Xiang, Shen, Zhe, Gong, Zhitao, Wei, Zichuan, Cotruta, Victor, Kirk, Phoebe, Rao, Anand, Giang, Minh, Peran, Ludovic, Warkentin, Tris, Collins, Eli, Barral, Joelle, Ghahramani, Zoubin, Hadsell, Raia, Sculley, D., Banks, Jeanine, Dragan, Anca, Petrov, Slav, Vinyals, Oriol, Dean, Jeff, Hassabis, Demis, Kavukcuoglu, Koray, Farabet, Clement, Buchatskaya, Elena, Borgeaud, Sebastian, Fiedel, Noah, Joulin, Armand, Kenealy, Kathleen, Dadashi, Robert, Andreev, Alek
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the
Externí odkaz:
http://arxiv.org/abs/2408.00118
Autor:
Sessa, Pier Giuseppe, Dadashi, Robert, Hussenot, Léonard, Ferret, Johan, Vieillard, Nino, Ramé, Alexandre, Shariari, Bobak, Perrin, Sarah, Friesen, Abe, Cideron, Geoffrey, Girgin, Sertan, Stanczyk, Piotr, Michi, Andrea, Sinopalnikov, Danila, Ramos, Sabela, Héliou, Amélie, Severyn, Aliaksei, Hoffman, Matt, Momchev, Nikola, Bachem, Olivier
Reinforcement learning from human feedback (RLHF) is a key driver of quality and safety in state-of-the-art large language models. Yet, a surprisingly simple and strong inference-time strategy is Best-of-N sampling that selects the best generation am
Externí odkaz:
http://arxiv.org/abs/2407.14622
Autor:
Ramé, Alexandre, Ferret, Johan, Vieillard, Nino, Dadashi, Robert, Hussenot, Léonard, Cedoz, Pierre-Louis, Sessa, Pier Giuseppe, Girgin, Sertan, Douillard, Arthur, Bachem, Olivier
Reinforcement learning from human feedback (RLHF) aligns large language models (LLMs) by encouraging their generations to have high rewards, using a reward model trained on human preferences. To prevent the forgetting of pre-trained knowledge, RLHF u
Externí odkaz:
http://arxiv.org/abs/2406.16768
Autor:
Ramesh, Shyam Sundhar, Hu, Yifan, Chaimalas, Iason, Mehta, Viraj, Sessa, Pier Giuseppe, Ammar, Haitham Bou, Bogunovic, Ilija
Adapting large language models (LLMs) for specific tasks usually involves fine-tuning through reinforcement learning with human feedback (RLHF) on preference data. While these data often come from diverse labelers' groups (e.g., different demographic
Externí odkaz:
http://arxiv.org/abs/2405.20304
Autor:
Botev, Aleksandar, De, Soham, Smith, Samuel L, Fernando, Anushan, Muraru, George-Cristian, Haroun, Ruba, Berrada, Leonard, Pascanu, Razvan, Sessa, Pier Giuseppe, Dadashi, Robert, Hussenot, Léonard, Ferret, Johan, Girgin, Sertan, Bachem, Olivier, Andreev, Alek, Kenealy, Kathleen, Mesnard, Thomas, Hardin, Cassidy, Bhupatiraju, Surya, Pathak, Shreya, Sifre, Laurent, Rivière, Morgane, Kale, Mihir Sanjay, Love, Juliette, Tafti, Pouya, Joulin, Armand, Fiedel, Noah, Senter, Evan, Chen, Yutian, Srinivasan, Srivatsan, Desjardins, Guillaume, Budden, David, Doucet, Arnaud, Vikram, Sharad, Paszke, Adam, Gale, Trevor, Borgeaud, Sebastian, Chen, Charlie, Brock, Andy, Paterson, Antonia, Brennan, Jenny, Risdal, Meg, Gundluru, Raj, Devanathan, Nesh, Mooney, Paul, Chauhan, Nilay, Culliton, Phil, Martins, Luiz Gustavo, Bandy, Elisa, Huntsperger, David, Cameron, Glenn, Zucker, Arthur, Warkentin, Tris, Peran, Ludovic, Giang, Minh, Ghahramani, Zoubin, Farabet, Clément, Kavukcuoglu, Koray, Hassabis, Demis, Hadsell, Raia, Teh, Yee Whye, de Frietas, Nando
We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve excellent performance on language. It has a fixed-sized state, which red
Externí odkaz:
http://arxiv.org/abs/2404.07839
Autor:
Gemma Team, Mesnard, Thomas, Hardin, Cassidy, Dadashi, Robert, Bhupatiraju, Surya, Pathak, Shreya, Sifre, Laurent, Rivière, Morgane, Kale, Mihir Sanjay, Love, Juliette, Tafti, Pouya, Hussenot, Léonard, Sessa, Pier Giuseppe, Chowdhery, Aakanksha, Roberts, Adam, Barua, Aditya, Botev, Alex, Castro-Ros, Alex, Slone, Ambrose, Héliou, Amélie, Tacchetti, Andrea, Bulanova, Anna, Paterson, Antonia, Tsai, Beth, Shahriari, Bobak, Lan, Charline Le, Choquette-Choo, Christopher A., Crepy, Clément, Cer, Daniel, Ippolito, Daphne, Reid, David, Buchatskaya, Elena, Ni, Eric, Noland, Eric, Yan, Geng, Tucker, George, Muraru, George-Christian, Rozhdestvenskiy, Grigory, Michalewski, Henryk, Tenney, Ian, Grishchenko, Ivan, Austin, Jacob, Keeling, James, Labanowski, Jane, Lespiau, Jean-Baptiste, Stanway, Jeff, Brennan, Jenny, Chen, Jeremy, Ferret, Johan, Chiu, Justin, Mao-Jones, Justin, Lee, Katherine, Yu, Kathy, Millican, Katie, Sjoesund, Lars Lowe, Lee, Lisa, Dixon, Lucas, Reid, Machel, Mikuła, Maciej, Wirth, Mateo, Sharman, Michael, Chinaev, Nikolai, Thain, Nithum, Bachem, Olivier, Chang, Oscar, Wahltinez, Oscar, Bailey, Paige, Michel, Paul, Yotov, Petko, Chaabouni, Rahma, Comanescu, Ramona, Jana, Reena, Anil, Rohan, McIlroy, Ross, Liu, Ruibo, Mullins, Ryan, Smith, Samuel L, Borgeaud, Sebastian, Girgin, Sertan, Douglas, Sholto, Pandya, Shree, Shakeri, Siamak, De, Soham, Klimenko, Ted, Hennigan, Tom, Feinberg, Vlad, Stokowiec, Wojciech, Chen, Yu-hui, Ahmed, Zafarali, Gong, Zhitao, Warkentin, Tris, Peran, Ludovic, Giang, Minh, Farabet, Clément, Vinyals, Oriol, Dean, Jeff, Kavukcuoglu, Koray, Hassabis, Demis, Ghahramani, Zoubin, Eck, Douglas, Barral, Joelle, Pereira, Fernando, Collins, Eli, Joulin, Armand, Fiedel, Noah, Senter, Evan, Andreev, Alek, Kenealy, Kathleen
This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate strong performance across academic benchmarks for language understanding,
Externí odkaz:
http://arxiv.org/abs/2403.08295
Hyper-redundant Robotic Manipulators (HRMs) offer great dexterity and flexibility of operation, but solving Inverse Kinematics (IK) is challenging. In this work, we introduce VO-FABRIK, an algorithm combining Forward and Backward Reaching Inverse Kin
Externí odkaz:
http://arxiv.org/abs/2403.05399
Autor:
Quaglia, L., Abbrescia, M., Aielli, G., Aly, R., Arena, M. C., Barroso, M., Benussi, L., Bianco, S., Boscherini, D., Bordon, F., Bruni, A., Buontempo, S., Busato, M., Camarri, P., Cardarelli, R., Congedo, L., Damiao, D. De Jesus, De Serio, M., Di Ciaccio, A., Di Stante, L., Dupieux, P., Eysermans, J., Ferretti, A., Galati, G., Gagliardi, M., Guida, R., Iaselli, G., Joly, B., Juks, S. A., Lee, K. S., Liberti, B., Ramirez, D. Lucero, Mandelli, B., Manen, S. P., Massa, L., Pastore, A., Pastori, E., Piccolo, D., Pizzimento, L., Polini, A., Proto, G., Pugliese, G., Ramos, D., Rigoletti, G., Rocchi, A., Romano, M., Samalan, A., Salvini, P., Santonico, R., Saviano, G., Sessa, M., Simone, S., Terlizzi, L., Tytgat, M., Vercellin, E., Verzeroli, M., Zaganidis, N.
ALICE (A Large Ion Collider Experiment) studies the Quark-Gluon Plasma (QGP): a deconfined state of matter obtained in ultra-relativistic heavy-ion collisions. One of the probes for QGP study are quarkonia and open heavy flavour, of which ALICE explo
Externí odkaz:
http://arxiv.org/abs/2402.19408