Zobrazeno 1 - 10
of 3 411
pro vyhledávání: '"Ahmed, A. K. A."'
Autor:
Wu, Junda, Lyu, Hanjia, Xia, Yu, Zhang, Zhehao, Barrow, Joe, Kumar, Ishita, Mirtaheri, Mehrnoosh, Chen, Hongjie, Rossi, Ryan A., Dernoncourt, Franck, Yu, Tong, Zhang, Ruiyi, Gu, Jiuxiang, Ahmed, Nesreen K., Wang, Yu, Chen, Xiang, Deilamsalehy, Hanieh, Park, Namyong, Kim, Sungchul, Yang, Huanrui, Mitra, Subrata, Hu, Zhengmian, Lipka, Nedim, Nguyen, Dang, Zhao, Yue, Luo, Jiebo, McAuley, Julian
Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities, such as text, images, and audio, to perform complex tasks with high accuracy. Thi
Externí odkaz:
http://arxiv.org/abs/2412.02142
Autor:
Pahilajani, Anish, Trivedi, Devasha, Shuai, Jincen, Yone, Khin S., Jain, Samyak Rajesh, Park, Namyong, Rossi, Ryan A., Ahmed, Nesreen K., Dernoncourt, Franck, Wang, Yu
Large Language Models (LLMs) have excelled in multi-hop question-answering (M-QA) due to their advanced reasoning abilities. However, the impact of the inherent reasoning structures on LLM M-QA performance remains unclear, largely due to the absence
Externí odkaz:
http://arxiv.org/abs/2411.00369
Autor:
TehraniJamsaz, Ali, Bhattacharjee, Arijit, Chen, Le, Ahmed, Nesreen K., Yazdanbakhsh, Amir, Jannesari, Ali
Recent advancements in Large Language Models (LLMs) have renewed interest in automatic programming language translation. Encoder-decoder transformer models, in particular, have shown promise in translating between different programming languages. How
Externí odkaz:
http://arxiv.org/abs/2410.20527
Autor:
Van Nguyen, Chien, Shen, Xuan, Aponte, Ryan, Xia, Yu, Basu, Samyadeep, Hu, Zhengmian, Chen, Jian, Parmar, Mihir, Kunapuli, Sasidhar, Barrow, Joe, Wu, Junda, Singh, Ashish, Wang, Yu, Gu, Jiuxiang, Dernoncourt, Franck, Ahmed, Nesreen K., Lipka, Nedim, Zhang, Ruiyi, Chen, Xiang, Yu, Tong, Kim, Sungchul, Deilamsalehy, Hanieh, Park, Namyong, Rimer, Mike, Zhang, Zhehao, Yang, Huanrui, Rossi, Ryan A., Nguyen, Thien Huu
Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources, making them ideal for various settings including on-device, mobile, edge d
Externí odkaz:
http://arxiv.org/abs/2410.20011
Autor:
Bernárdez, Guillermo, Telyatnikov, Lev, Montagna, Marco, Baccini, Federica, Papillon, Mathilde, Ferriol-Galmés, Miquel, Hajij, Mustafa, Papamarkou, Theodore, Bucarelli, Maria Sofia, Zaghen, Olga, Mathe, Johan, Myers, Audun, Mahan, Scott, Lillemark, Hansen, Vadgama, Sharvaree, Bekkers, Erik, Doster, Tim, Emerson, Tegan, Kvinge, Henry, Agate, Katrina, Ahmed, Nesreen K, Bai, Pengfei, Banf, Michael, Battiloro, Claudio, Beketov, Maxim, Bogdan, Paul, Carrasco, Martin, Cavallo, Andrea, Choi, Yun Young, Dasoulas, George, Elphick, Matouš, Escalona, Giordan, Filipiak, Dominik, Fritze, Halley, Gebhart, Thomas, Gil-Sorribes, Manel, Goomanee, Salvish, Guallar, Victor, Imasheva, Liliya, Irimia, Andrei, Jin, Hongwei, Johnson, Graham, Kanakaris, Nikos, Koloski, Boshko, Kovač, Veljko, Lecha, Manuel, Lee, Minho, Leroy, Pierrick, Long, Theodore, Magai, German, Martinez, Alvaro, Masden, Marissa, Mežnar, Sebastian, Miquel-Oliver, Bertran, Molina, Alexis, Nikitin, Alexander, Nurisso, Marco, Piekenbrock, Matt, Qin, Yu, Rygiel, Patryk, Salatiello, Alessandro, Schattauer, Max, Snopov, Pavel, Suk, Julian, Sánchez, Valentina, Tec, Mauricio, Vaccarino, Francesco, Verhellen, Jonas, Wantiez, Frederic, Weers, Alexander, Zajec, Patrik, Škrlj, Blaž, Miolane, Nina
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem
Externí odkaz:
http://arxiv.org/abs/2409.05211
Autor:
Wang, Yu, Rossi, Ryan A., Park, Namyong, Chen, Huiyuan, Ahmed, Nesreen K., Trivedi, Puja, Dernoncourt, Franck, Koutra, Danai, Derr, Tyler
Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno are trained on a huge amount of language corpus, images, videos, and audio that are extremely diverse from numerous domains. This training paradigm over diverse well-curated
Externí odkaz:
http://arxiv.org/abs/2406.05109
Autor:
Duan, Shukai, Ping, Heng, Kanakaris, Nikos, Xiao, Xiongye, Zhang, Peiyu, Kyriakis, Panagiotis, Ahmed, Nesreen K., Ma, Guixiang, Capota, Mihai, Nazarian, Shahin, Willke, Theodore L., Bogdan, Paul
Existing approaches for device placement ignore the topological features of computation graphs and rely mostly on heuristic methods for graph partitioning. At the same time, they either follow a grouper-placer or an encoder-placer architecture, which
Externí odkaz:
http://arxiv.org/abs/2405.14185
Although Large Language Models (LLMs) excel at addressing straightforward reasoning tasks, they frequently struggle with difficulties when confronted by more complex multi-step reasoning due to a range of factors. Firstly, natural language often enco
Externí odkaz:
http://arxiv.org/abs/2402.13415
Autor:
Chen, Le, Ahmed, Nesreen K., Dutta, Akash, Bhattacharjee, Arijit, Yu, Sixing, Mahmud, Quazi Ishtiaque, Abebe, Waqwoya, Phan, Hung, Sarkar, Aishwarya, Butler, Branden, Hasabnis, Niranjan, Oren, Gal, Vo, Vy A., Munoz, Juan Pablo, Willke, Theodore L., Mattson, Tim, Jannesari, Ali
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning. Both encoder-decoder models and prompt-based techniques have shown immense potential for natural language processing and code-ba
Externí odkaz:
http://arxiv.org/abs/2402.02018
Autor:
Duan, Shukai, Kanakaris, Nikos, Xiao, Xiongye, Ping, Heng, Zhou, Chenyu, Ahmed, Nesreen K., Ma, Guixiang, Capota, Mihai, Willke, Theodore L., Nazarian, Shahin, Bogdan, Paul
Code optimization is a daunting task that requires a significant level of expertise from experienced programmers. This level of expertise is not sufficient when compared to the rapid development of new hardware architectures. Towards advancing the wh
Externí odkaz:
http://arxiv.org/abs/2312.05657