Zobrazeno 1 - 10
of 11 690
pro vyhledávání: '"A Tejaswini"'
Autor:
Niranjana, Tejaswini1 iacsiacsiacs@gmail.com, Kim, Soyoung2, Chow, Yiu Fai3, Ip, Kimho4, Wang, Andy Chih-Ming5, Morris, Meaghan6
Publikováno v:
Inter-Asia Cultural Studies. Mar2021, Vol. 22 Issue 1, p100-117. 18p. 3 Color Photographs.
Autor:
Padhi, Inkit, Nagireddy, Manish, Cornacchia, Giandomenico, Chaudhury, Subhajit, Pedapati, Tejaswini, Dognin, Pierre, Murugesan, Keerthiram, Miehling, Erik, Cooper, Martín Santillán, Fraser, Kieran, Zizzo, Giulio, Hameed, Muhammad Zaid, Purcell, Mark, Desmond, Michael, Pan, Qian, Vejsbjerg, Inge, Daly, Elizabeth M., Hind, Michael, Geyer, Werner, Rawat, Ambrish, Varshney, Kush R., Sattigeri, Prasanna
We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with any large language model (LLM). These models offer comprehensive cover
Externí odkaz:
http://arxiv.org/abs/2412.07724
While being very successful in solving many downstream tasks, the application of deep neural networks is limited in real-life scenarios because of their susceptibility to domain shifts such as common corruptions, and adversarial attacks. The existenc
Externí odkaz:
http://arxiv.org/abs/2411.19853
Autor:
Medi, Tejaswini, Rampini, Arianna, Reddy, Pradyumna, Jayaraman, Pradeep Kumar, Keuper, Margret
Autoregressive (AR) models have achieved remarkable success in natural language and image generation, but their application to 3D shape modeling remains largely unexplored. Unlike diffusion models, AR models enable more efficient and controllable gen
Externí odkaz:
http://arxiv.org/abs/2411.19037
Autor:
Agarwal, Amit, Patel, Hitesh, Pattnayak, Priyaranjan, Panda, Srikant, Kumar, Bhargava, Kumar, Tejaswini
Publikováno v:
IJERT, Volume 13, Issue 10, October 2024
The development of robust Document AI models has been constrained by limited access to high-quality, labeled datasets, primarily due to data privacy concerns, scarcity, and the high cost of manual annotation. Traditional methods of synthetic data gen
Externí odkaz:
http://arxiv.org/abs/2412.03590
Deep neural networks are susceptible to adversarial attacks and common corruptions, which undermine their robustness. In order to enhance model resilience against such challenges, Adversarial Training (AT) has emerged as a prominent solution. Neverth
Externí odkaz:
http://arxiv.org/abs/2410.23142
Autor:
Ko, Ching-Yun, Chen, Pin-Yu, Das, Payel, Mroueh, Youssef, Dan, Soham, Kollias, Georgios, Chaudhury, Subhajit, Pedapati, Tejaswini, Daniel, Luca
Reducing the likelihood of generating harmful and toxic output is an essential task when aligning large language models (LLMs). Existing methods mainly rely on training an external reward model (i.e., another language model) or fine-tuning the LLM us
Externí odkaz:
http://arxiv.org/abs/2410.03818
Autor:
Ashktorab, Zahra, Desmond, Michael, Pan, Qian, Johnson, James M., Cooper, Martin Santillan, Daly, Elizabeth M., Nair, Rahul, Pedapati, Tejaswini, Achintalwar, Swapnaja, Geyer, Werner
Evaluation of large language model (LLM) outputs requires users to make critical judgments about the best outputs across various configurations. This process is costly and takes time given the large amounts of data. LLMs are increasingly used as eval
Externí odkaz:
http://arxiv.org/abs/2410.00873
Autor:
Bareliya, Rajendra Singh1 bareliyarajendra1m@gmail.com, Thakur, Satyendra Singh1, Sharma, Hari Om1
Publikováno v:
Quarterly Research Journal of Plant & Animal Sciences / Bhartiya Krishi Anusandhan Patrika. Mar2019, Vol. 34 Issue 1, p28-32. 5p.
Autor:
Khatiwada, Aamod, Kokel, Harsha, Abdelaziz, Ibrahim, Chaudhury, Subhajit, Dolby, Julian, Hassanzadeh, Oktie, Huang, Zhenhan, Pedapati, Tejaswini, Samulowitz, Horst, Srinivas, Kavitha
Enterprises have a growing need to identify relevant tables in data lakes; e.g. tables that are unionable, joinable, or subsets of each other. Tabular neural models can be helpful for such data discovery tasks. In this paper, we present TabSketchFM,
Externí odkaz:
http://arxiv.org/abs/2407.01619