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pro vyhledávání: '"Srikanth"'
Autor:
Bozkurt, Burcu1,2, Planey, Arrianna Marie1,2, Aijaz, Monisa1,2, Weinstein, Joshua M.1,2, Cilenti, Dorothy3, Shea, Christopher M.1,2, Khairat, Saif2,4 Saif@unc.edu
Publikováno v:
Permanente Journal. 6/14/2024, Vol. 28 Issue 2, p36-46. 11p.
Publikováno v:
Alg. Number Th. 15 (2021) 1157-1180
We investigate cohomological support varieties for finite-dimensional Lie superalgebras defined over fields of odd characteristic. Verifying a conjecture from our previous work, we show the support variety of a finite-dimensional supermodule can be r
Externí odkaz:
http://arxiv.org/abs/1912.07117
Akademický článek
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Autor:
Mathai, Alex, Sedamaki, Kranthi, Das, Debeshee, Mathews, Noble Saji, Tamilselvam, Srikanth, Chimalakonda, Sridhar, Kumar, Atul
Machine Learning (ML) for software engineering (SE) has gained prominence due to its ability to significantly enhance the performance of various SE applications. This progress is largely attributed to the development of generalizable source code repr
Externí odkaz:
http://arxiv.org/abs/2411.14611
Autor:
Amireddy, Prashanth, Behera, Amik Raj, Paraashar, Manaswi, Srinivasan, Srikanth, Sudan, Madhu
In this work, we show that the class of multivariate degree-$d$ polynomials mapping $\{0,1\}^{n}$ to any Abelian group $G$ is locally correctable with $\widetilde{O}_{d}((\log n)^{d})$ queries for up to a fraction of errors approaching half the minim
Externí odkaz:
http://arxiv.org/abs/2411.07374
We develop a correspondence between presentations of compactly generated triangulated categories as localizations of derived categories of ring spectra and proxy-small objects, and explore some consequences. In addition, we give a characterization of
Externí odkaz:
http://arxiv.org/abs/2411.07328
We propose two new methods for multi-document financial question answering. First, a method that uses semantic tagging, and then, queries the index to get the context (RAG_SEM). And second, a Knowledge Graph (KG_RAG) based method that uses semantic t
Externí odkaz:
http://arxiv.org/abs/2411.07264
Autor:
Cai, Feiyang, Zhu, Tianyu, Tzeng, Tzuen-Rong, Duan, Yongping, Liu, Ling, Pilla, Srikanth, Li, Gang, Luo, Feng
Artificial intelligence (AI) has significantly advanced computational chemistry research. However, traditional AI methods often rely on task-specific model designs and training, which constrain both the scalability of model size and generalization ac
Externí odkaz:
http://arxiv.org/abs/2410.21422
Efficiently deriving structured workflows from unannotated dialogs remains an underexplored and formidable challenge in computational linguistics. Automating this process could significantly accelerate the manual design of workflows in new domains an
Externí odkaz:
http://arxiv.org/abs/2410.18481
Autor:
Shahriar, Sadat, Qi, Zheng, Pappas, Nikolaos, Doss, Srikanth, Sunkara, Monica, Halder, Kishaloy, Mager, Manuel, Benajiba, Yassine
Aligning Large Language Models (LLM) to address subjectivity and nuanced preference levels requires adequate flexibility and control, which can be a resource-intensive and time-consuming procedure. Existing training-time alignment methods require ful
Externí odkaz:
http://arxiv.org/abs/2410.19206