Zobrazeno 1 - 10
of 9 795
pro vyhledávání: '"Obando, A."'
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
Autor:
de Melo, Fernando, Carvalho, Gabriel Dias, Correia, Pedro S., Obando, Paola Concha, de Oliveira, Thiago R., Vallejos, Raúl O.
Quantum mechanics started out as a theory to describe the smallest scales of energy in Nature. After hundred years of development it is now routinely employed to describe, for example, quantum computers with thousands of qubits. This tremendous progr
Externí odkaz:
http://arxiv.org/abs/2411.07327
Autor:
Medina-Segura, Edgar, Obando, Paola C., Mkhumbuza, Light, Galvez, Enrique J., Rosales-Guzmán, Carmelo, Ruffato, Gianluca, Romanato, Filippo, Forbes, Andrew, Nape, Isaac
Maxwell's demon (MD) has proven an instructive vehicle by which to explore the relationship between information theory and thermodynamics, fueling the possibility of information driven machines. A long standing debate has been the concern of entropy
Externí odkaz:
http://arxiv.org/abs/2411.03893
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:
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
Autor:
Bezuidenhout, Hadrian, Koni, Mwezi, Leach, Jonathan, Obando, Paola Concha, Forbes, Andrew, Nape, Isaac
Structured light, light tailored in its internal degrees of freedom, has become topical in numerous quantum and classical information processing protocols. In this work, we harness the high dimensional nature of structured light modulated in the tran
Externí odkaz:
http://arxiv.org/abs/2406.05727
Publikováno v:
Phys.Rev.A 109, 052219 (2024)
We investigate the dynamics of ergotropy in open systems under Markovian and non-Markovian evolutions. In this scenario, we begin by formulating the ergotropy of an arbitrary qubit state in terms of energy and coherence. Thus, we determine the condit
Externí odkaz:
http://arxiv.org/abs/2403.04698