Zobrazeno 1 - 10
of 25 213
pro vyhledávání: '"Zaheer, A."'
Autor:
Khan, Asifullah, Sohail, Anabia, Fiaz, Mustansar, Hassan, Mehdi, Afridi, Tariq Habib, Marwat, Sibghat Ullah, Munir, Farzeen, Ali, Safdar, Naseem, Hannan, Zaheer, Muhammad Zaigham, Ali, Kamran, Sultana, Tangina, Tanoli, Ziaurrehman, Akhter, Naeem
Deep supervised learning models require high volume of labeled data to attain sufficiently good results. Although, the practice of gathering and annotating such big data is costly and laborious. Recently, the application of self supervised learning (
Externí odkaz:
http://arxiv.org/abs/2408.17059
Modern ML systems increasingly augment input instances with additional relevant information to enhance final prediction. Despite growing interest in such retrieval-augmented models, their fundamental properties and training are not well understood. W
Externí odkaz:
http://arxiv.org/abs/2408.15399
Autor:
Godbole, Ameya, Monath, Nicholas, Kim, Seungyeon, Rawat, Ankit Singh, McCallum, Andrew, Zaheer, Manzil
In text generation, hallucinations refer to the generation of seemingly coherent text that contradicts established knowledge. One compelling hypothesis is that hallucinations occur when a language model is given a generation task outside its parametr
Externí odkaz:
http://arxiv.org/abs/2408.10490
Autor:
Saeed, Muhammad Saad, Nawaz, Shah, Zaheer, Muhammad Zaigham, Khan, Muhammad Haris, Nandakumar, Karthik, Yousaf, Muhammad Haroon, Sajjad, Hassan, De Schepper, Tom, Schedl, Markus
Multimodal networks have demonstrated remarkable performance improvements over their unimodal counterparts. Existing multimodal networks are designed in a multi-branch fashion that, due to the reliance on fusion strategies, exhibit deteriorated perfo
Externí odkaz:
http://arxiv.org/abs/2408.07445
Autor:
Shi, Kensen, Altınbüken, Deniz, Anand, Saswat, Christodorescu, Mihai, Grünwedel, Katja, Koenings, Alexa, Naidu, Sai, Pathak, Anurag, Rasi, Marc, Ribeiro, Fredde, Ruffin, Brandon, Sanyam, Siddhant, Tabachnyk, Maxim, Toth, Sara, Tu, Roy, Welp, Tobias, Yin, Pengcheng, Zaheer, Manzil, Chandra, Satish, Sutton, Charles
We propose using natural language outlines as a novel modality and interaction surface for providing AI assistance to developers throughout the software development process. An NL outline for a code function comprises multiple statements written in c
Externí odkaz:
http://arxiv.org/abs/2408.04820
Autor:
Liaqat, Muhammad Irzam, Nawaz, Shah, Zaheer, Muhammad Zaigham, Saeed, Muhammad Saad, Sajjad, Hassan, De Schepper, Tom, Nandakumar, Karthik, Schedl, Muhammad Haris Khan Markus
Multimodal learning has demonstrated remarkable performance improvements over unimodal architectures. However, multimodal learning methods often exhibit deteriorated performances if one or more modalities are missing. This may be attributed to the co
Externí odkaz:
http://arxiv.org/abs/2407.16243
Autor:
Ravindranath, Sai Srivatsa, Feng, Zhe, Wang, Di, Zaheer, Manzil, Mehta, Aranyak, Parkes, David C.
Revenue-optimal auction design is a challenging problem with significant theoretical and practical implications. Sequential auction mechanisms, known for their simplicity and strong strategyproofness guarantees, are often limited by theoretical resul
Externí odkaz:
http://arxiv.org/abs/2407.08022
The available software to study the spectroscopic characteristics of atoms, ions, and molecules runs on a server, e.g., the general-purpose atomic structure package (GRASP) and R-matrix method. A Python program has been developed to compute Transitio
Externí odkaz:
http://arxiv.org/abs/2405.19663
Publikováno v:
Scientific Reports 14, 13005 (2024)
The dynamics of nonlinear ion-acoustic solitary waves in the presence of kinetic (Landau type) damping have been investigated in a collisionless, non-magnetized electron-ion plasma. A cold ion fluid model, coupled to a Vlasov-type kinetic equation fo
Externí odkaz:
http://arxiv.org/abs/2406.03267
Publikováno v:
Neural Computing and Applications, pp.1-17 (2024)
Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or representations of the normal training data. At test time, the trained AE
Externí odkaz:
http://arxiv.org/abs/2405.05886