Zobrazeno 1 - 10
of 269
pro vyhledávání: '"Mousavi, Ali"'
Autor:
Pradeep, Ronak, Lee, Daniel, Mousavi, Ali, Pound, Jeff, Sang, Yisi, Lin, Jimmy, Ilyas, Ihab, Potdar, Saloni, Arefiyan, Mostafa, Li, Yunyao
The rapid advancement of Large Language Models (LLMs) and conversational assistants necessitates dynamic, scalable, and configurable conversational datasets for training and evaluation. These datasets must accommodate diverse user interaction modes,
Externí odkaz:
http://arxiv.org/abs/2408.05948
Autor:
Ge, Xiou, Mousavi, Ali, Grave, Edouard, Joulin, Armand, Qian, Kun, Han, Benjamin, Arefiyan, Mostafa, Li, Yunyao
Large Language Models (LLMs) have demonstrated impressive capability in different tasks and are bringing transformative changes to many domains. However, keeping the knowledge in LLMs up-to-date remains a challenge once pretraining is complete. It is
Externí odkaz:
http://arxiv.org/abs/2406.04496
Autor:
Wang, Junxiong, Mousavi, Ali, Attia, Omar, Pradeep, Ronak, Potdar, Saloni, Rush, Alexander M., Minhas, Umar Farooq, Li, Yunyao
Entity disambiguation (ED), which links the mentions of ambiguous entities to their referent entities in a knowledge base, serves as a core component in entity linking (EL). Existing generative approaches demonstrate improved accuracy compared to cla
Externí odkaz:
http://arxiv.org/abs/2404.01626
Publikováno v:
Meform 2018, 21.-23.03.2018
In diesem Paper werden Untersuchungen eines Tiefziehwerkzeugs aus Mineralguss vorgestellt. Der Grund für die Verwendung von Mineralguss als alternativen Werkstoff für schnelle Werkzeuge liegt in den relativ geringen Initialkosten zur Herstellung un
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A30944
https://tud.qucosa.de/api/qucosa%3A30944/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A30944/attachment/ATT-0/
Autor:
Mousavi, Ali, Zhan, Xin, Bai, He, Shi, Peng, Rekatsinas, Theo, Han, Benjamin, Li, Yunyao, Pound, Jeff, Susskind, Josh, Schluter, Natalie, Ilyas, Ihab, Jaitly, Navdeep
Datasets that pair Knowledge Graphs (KG) and text together (KG-T) can be used to train forward and reverse neural models that generate text from KG and vice versa. However models trained on datasets where KG and text pairs are not equivalent can suff
Externí odkaz:
http://arxiv.org/abs/2309.11669
Autor:
Ilyas, Ihab F., Lacerda, JP, Li, Yunyao, Minhas, Umar Farooq, Mousavi, Ali, Pound, Jeffrey, Rekatsinas, Theodoros, Sumanth, Chiraag
Applications of large open-domain knowledge graphs (KGs) to real-world problems pose many unique challenges. In this paper, we present extensions to Saga our platform for continuous construction and serving of knowledge at scale. In particular, we de
Externí odkaz:
http://arxiv.org/abs/2305.09464
Autor:
Mohoney, Jason, Pacaci, Anil, Chowdhury, Shihabur Rahman, Mousavi, Ali, Ilyas, Ihab F., Minhas, Umar Farooq, Pound, Jeffrey, Rekatsinas, Theodoros
There is an increasing adoption of machine learning for encoding data into vectors to serve online recommendation and search use cases. As a result, recent data management systems propose augmenting query processing with online vector similarity sear
Externí odkaz:
http://arxiv.org/abs/2304.01926
Publikováno v:
in IEEE Signal Processing Letters, vol. 28, pp. 713-717, 2021
Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for approximating integrals, for instance in the context of Bayesian inference. In IS, the samples are simulated from the so-called proposal distribution, and the choice of this prop
Externí odkaz:
http://arxiv.org/abs/2209.13716
Autor:
Mousavi, Ali, Hedayatnia, Ali, van Vliet, Patrick Piet, Dartora, Daniela Ravizzoni, Wong, Nicholas, Rafatian, Naimeh, Nuyt, Anne Monique, Moraes, Christopher, Ajji, Abdellah, Andelfinger, Gregor, Savoji, Houman
Publikováno v:
In Applied Materials Today February 2024 36