Zobrazeno 1 - 10
of 23 697
pro vyhledávání: '"Berrada A"'
Autor:
Hall, Melissa, Mañas, Oscar, Askari-Hemmat, Reyhane, Ibrahim, Mark, Ross, Candace, Astolfi, Pietro, Ifriqi, Tariq Berrada, Havasi, Marton, Benchetrit, Yohann, Ullrich, Karen, Braga, Carolina, Charnalia, Abhishek, Ryan, Maeve, Rabbat, Mike, Drozdzal, Michal, Verbeek, Jakob, Romero-Soriano, Adriana
As the use of text-to-image generative models increases, so does the adoption of automatic benchmarking methods used in their evaluation. However, while metrics and datasets abound, there are few unified benchmarking libraries that provide a framewor
Externí odkaz:
http://arxiv.org/abs/2412.10604
Autor:
Berrada, Tariq, Astolfi, Pietro, Verbeek, Jakob, Hall, Melissa, Havasi, Marton, Drozdzal, Michal, Benchetrit, Yohann, Romero-Soriano, Adriana, Alahari, Karteek
Latent diffusion models (LDMs) power state-of-the-art high-resolution generative image models. LDMs learn the data distribution in the latent space of an autoencoder (AE) and produce images by mapping the generated latents into RGB image space using
Externí odkaz:
http://arxiv.org/abs/2411.04873
Autor:
Ifriqi, Tariq Berrada, Astolfi, Pietro, Hall, Melissa, Askari-Hemmat, Reyhane, Benchetrit, Yohann, Havasi, Marton, Muckley, Matthew, Alahari, Karteek, Romero-Soriano, Adriana, Verbeek, Jakob, Drozdzal, Michal
Large-scale training of latent diffusion models (LDMs) has enabled unprecedented quality in image generation. However, the key components of the best performing LDM training recipes are oftentimes not available to the research community, preventing a
Externí odkaz:
http://arxiv.org/abs/2411.03177
Autor:
Talafha, Bashar, Kadaoui, Karima, Magdy, Samar Mohamed, Habiboullah, Mariem, Chafei, Chafei Mohamed, El-Shangiti, Ahmed Oumar, Zayed, Hiba, tourad, Mohamedou cheikh, Alhamouri, Rahaf, Assi, Rwaa, Alraeesi, Aisha, Mohamed, Hour, Alwajih, Fakhraddin, Mohamed, Abdelrahman, Mekki, Abdellah El, Nagoudi, El Moatez Billah, Saadia, Benelhadj Djelloul Mama, Alsayadi, Hamzah A., Al-Dhabyani, Walid, Shatnawi, Sara, Ech-Chammakhy, Yasir, Makouar, Amal, Berrachedi, Yousra, Jarrar, Mustafa, Shehata, Shady, Berrada, Ismail, Abdul-Mageed, Muhammad
In spite of the recent progress in speech processing, the majority of world languages and dialects remain uncovered. This situation only furthers an already wide technological divide, thereby hindering technological and socioeconomic inclusion. This
Externí odkaz:
http://arxiv.org/abs/2410.04527
Autor:
Mahdaouy, Abdelkader El, Lamsiyah, Salima, Idrissi, Meryem Janati, Alami, Hamza, Yartaoui, Zakaria, Berrada, Ismail
Detecting and classifying suspicious or malicious domain names and URLs is fundamental task in cybersecurity. To leverage such indicators of compromise, cybersecurity vendors and practitioners often maintain and update blacklists of known malicious d
Externí odkaz:
http://arxiv.org/abs/2409.09143
Autor:
Ghalebikesabi, Sahra, Bagdasaryan, Eugene, Yi, Ren, Yona, Itay, Shumailov, Ilia, Pappu, Aneesh, Shi, Chongyang, Weidinger, Laura, Stanforth, Robert, Berrada, Leonard, Kohli, Pushmeet, Huang, Po-Sen, Balle, Borja
Advanced AI assistants combine frontier LLMs and tool access to autonomously perform complex tasks on behalf of users. While the helpfulness of such assistants can increase dramatically with access to user information including emails and documents,
Externí odkaz:
http://arxiv.org/abs/2408.02373
Autor:
Malaysha, Sanad, El-Haj, Mo, Ezzini, Saad, Khalilia, Mohammed, Jarrar, Mustafa, Almujaiwel, Sultan, Berrada, Ismail, Bouamor, Houda
The expanding financial markets of the Arab world require sophisticated Arabic NLP tools. To address this need within the banking domain, the Arabic Financial NLP (AraFinNLP) shared task proposes two subtasks: (i) Multi-dialect Intent Detection and (
Externí odkaz:
http://arxiv.org/abs/2407.09818
Navigating the complexities of language diversity is a central challenge in developing robust natural language processing systems, especially in specialized domains like banking. The Moroccan Dialect (Darija) serves as the common language that blends
Externí odkaz:
http://arxiv.org/abs/2405.16482
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
