Zobrazeno 1 - 10
of 16 207
pro vyhledávání: '"A, Cerón"'
Autor:
Romanou, Angelika, Foroutan, Negar, Sotnikova, Anna, Chen, Zeming, Nelaturu, Sree Harsha, Singh, Shivalika, Maheshwary, Rishabh, Altomare, Micol, Haggag, Mohamed A., A, Snegha, Amayuelas, Alfonso, Amirudin, Azril Hafizi, Aryabumi, Viraat, Boiko, Danylo, Chang, Michael, Chim, Jenny, Cohen, Gal, Dalmia, Aditya Kumar, Diress, Abraham, Duwal, Sharad, Dzenhaliou, Daniil, Florez, Daniel Fernando Erazo, Farestam, Fabian, Imperial, Joseph Marvin, Islam, Shayekh Bin, Isotalo, Perttu, Jabbarishiviari, Maral, Karlsson, Börje F., Khalilov, Eldar, Klamm, Christopher, Koto, Fajri, Krzemiński, Dominik, de Melo, Gabriel Adriano, Montariol, Syrielle, Nan, Yiyang, Niklaus, Joel, Novikova, Jekaterina, Ceron, Johan Samir Obando, Paul, Debjit, Ploeger, Esther, Purbey, Jebish, Rajwal, Swati, Ravi, Selvan Sunitha, Rydell, Sara, Santhosh, Roshan, Sharma, Drishti, Skenduli, Marjana Prifti, Moakhar, Arshia Soltani, Moakhar, Bardia Soltani, Tamir, Ran, Tarun, Ayush Kumar, Wasi, Azmine Toushik, Weerasinghe, Thenuka Ovin, Yilmaz, Serhan, Zhang, Mike, Schlag, Imanol, Fadaee, Marzieh, Hooker, Sara, Bosselut, Antoine
The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities. However, the developmen
Externí odkaz:
http://arxiv.org/abs/2411.19799
Autor:
Vayani, Ashmal, Dissanayake, Dinura, Watawana, Hasindri, Ahsan, Noor, Sasikumar, Nevasini, Thawakar, Omkar, Ademtew, Henok Biadglign, Hmaiti, Yahya, Kumar, Amandeep, Kuckreja, Kartik, Maslych, Mykola, Ghallabi, Wafa Al, Mihaylov, Mihail, Qin, Chao, Shaker, Abdelrahman M, Zhang, Mike, Ihsani, Mahardika Krisna, Esplana, Amiel, Gokani, Monil, Mirkin, Shachar, Singh, Harsh, Srivastava, Ashay, Hamerlik, Endre, Izzati, Fathinah Asma, Maani, Fadillah Adamsyah, Cavada, Sebastian, Chim, Jenny, Gupta, Rohit, Manjunath, Sanjay, Zhumakhanova, Kamila, Rabevohitra, Feno Heriniaina, Amirudin, Azril, Ridzuan, Muhammad, Kareem, Daniya, More, Ketan, Li, Kunyang, Shakya, Pramesh, Saad, Muhammad, Ghasemaghaei, Amirpouya, Djanibekov, Amirbek, Azizov, Dilshod, Jankovic, Branislava, Bhatia, Naman, Cabrera, Alvaro, Obando-Ceron, Johan, Otieno, Olympiah, Farestam, Fabian, Rabbani, Muztoba, Baliah, Sanoojan, Sanjeev, Santosh, Shtanchaev, Abduragim, Fatima, Maheen, Nguyen, Thao, Kareem, Amrin, Aremu, Toluwani, Xavier, Nathan, Bhatkal, Amit, Toyin, Hawau, Chadha, Aman, Cholakkal, Hisham, Anwer, Rao Muhammad, Felsberg, Michael, Laaksonen, Jorma, Solorio, Thamar, Choudhury, Monojit, Laptev, Ivan, Shah, Mubarak, Khan, Salman, Khan, Fahad
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource
Externí odkaz:
http://arxiv.org/abs/2411.16508
Political discourse on Twitter is a moving target: politicians continuously make statements about their positions. It is therefore crucial to track their discourse on social media to understand their ideological positions and goals. However, Twitter
Externí odkaz:
http://arxiv.org/abs/2410.15743
The loss of plasticity in learning agents, analogous to the solidification of neural pathways in biological brains, significantly impedes learning and adaptation in reinforcement learning due to its non-stationary nature. To address this fundamental
Externí odkaz:
http://arxiv.org/abs/2410.07994
The use of deep neural networks in reinforcement learning (RL) often suffers from performance degradation as model size increases. While soft mixtures of experts (SoftMoEs) have recently shown promise in mitigating this issue for online RL, the reaso
Externí odkaz:
http://arxiv.org/abs/2410.01930
Autor:
Vélez-Ceron, Ignasi, Coelho, Rodrigo C. V., Guillamat, Pau, da Gama, Margarida Telo, Sagués, Francesc, Ignés-Mullol, Jordi
Microfluidics involves the manipulation of flows at the microscale, typically requiring external power sources to generate pressure gradients. Alternatively, harnessing flows from active fluids, which are usually chaotic, has been proposed as a parad
Externí odkaz:
http://arxiv.org/abs/2407.09960
Autor:
Ardaševa, Aleksandra, Vélez-Cerón, Ignasi, Pedersen, Martin Cramer, Ignés-Mullol, Jordi, Sagués, Francesc, Doostmohammadi, Amin
The field of active nematics has traditionally employed descriptions based on dipolar activity, with interactions that align along a single axis. However, it has been theoretically predicted that interactions with a substrate, prevalent in most biolo
Externí odkaz:
http://arxiv.org/abs/2407.03723
Autor:
McAleese, Nat, Pokorny, Rai Michael, Uribe, Juan Felipe Ceron, Nitishinskaya, Evgenia, Trebacz, Maja, Leike, Jan
Reinforcement learning from human feedback (RLHF) is fundamentally limited by the capacity of humans to correctly evaluate model output. To improve human evaluation ability and overcome that limitation this work trains "critic" models that help human
Externí odkaz:
http://arxiv.org/abs/2407.00215
Autor:
Willi, Timon, Obando-Ceron, Johan, Foerster, Jakob, Dziugaite, Karolina, Castro, Pablo Samuel
Mixtures of Experts (MoEs) have gained prominence in (self-)supervised learning due to their enhanced inference efficiency, adaptability to distributed training, and modularity. Previous research has illustrated that MoEs can significantly boost Deep
Externí odkaz:
http://arxiv.org/abs/2406.18420
Deep reinforcement learning (deep RL) has achieved tremendous success on various domains through a combination of algorithmic design and careful selection of hyper-parameters. Algorithmic improvements are often the result of iterative enhancements bu
Externí odkaz:
http://arxiv.org/abs/2406.17523