Zobrazeno 1 - 10
of 427
pro vyhledávání: '"P. Saikrishna"'
Retrieval-based question answering systems often suffer from positional bias, leading to suboptimal answer generation. We propose LoRE (Logit-Ranked Retriever Ensemble), a novel approach that improves answer accuracy and relevance by mitigating posit
Externí odkaz:
http://arxiv.org/abs/2410.10042
Autor:
Badrinarayanan, Saikrishna, Osoba, Osonde, Cheng, Miao, Rogers, Ryan, Jain, Sakshi, Tandra, Rahul, Pillai, Natesh S.
AI fairness measurements, including tests for equal treatment, often take the form of disaggregated evaluations of AI systems. Such measurements are an important part of Responsible AI operations. These measurements compare system performance across
Externí odkaz:
http://arxiv.org/abs/2409.04652
Autor:
Korablyov, Maksym, Liu, Cheng-Hao, Jain, Moksh, van der Sloot, Almer M., Jolicoeur, Eric, Ruediger, Edward, Nica, Andrei Cristian, Bengio, Emmanuel, Lapchevskyi, Kostiantyn, St-Cyr, Daniel, Schuetz, Doris Alexandra, Butoi, Victor Ion, Rector-Brooks, Jarrid, Blackburn, Simon, Feng, Leo, Nekoei, Hadi, Gottipati, SaiKrishna, Vijayan, Priyesh, Gupta, Prateek, Rampášek, Ladislav, Avancha, Sasikanth, Bacon, Pierre-Luc, Hamilton, William L., Paige, Brooks, Misra, Sanchit, Jastrzebski, Stanislaw Kamil, Kaul, Bharat, Precup, Doina, Hernández-Lobato, José Miguel, Segler, Marwin, Bronstein, Michael, Marinier, Anne, Tyers, Mike, Bengio, Yoshua
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active lear
Externí odkaz:
http://arxiv.org/abs/2405.01616
Autor:
Dakle, Parag Pravin, Gon, Alolika, Zha, Sihan, Wang, Liang, Rallabandi, SaiKrishna, Raghavan, Preethi
In this paper, we describe the different approaches explored by the Jetsons team for the Multi-Lingual ESG Impact Duration Inference (ML-ESG-3) shared task. The shared task focuses on predicting the duration and type of the ESG impact of a news artic
Externí odkaz:
http://arxiv.org/abs/2404.00386
Code smells indicate the potential problems of software quality so that developers can identify refactoring opportunities by detecting code smells. State-of-the-art approaches leverage heuristics, machine learning, and deep learning to detect code sm
Externí odkaz:
http://arxiv.org/abs/2402.10398
Publikováno v:
2023 IEEE International Conference on Data Mining Workshops (ICDMW), December 1-4, 2023, Shanghai, China
Knowledge graphs (KGs) have garnered significant attention for their vast potential across diverse domains. However, the issue of outdated facts poses a challenge to KGs, affecting their overall quality as real-world information evolves. Existing sol
Externí odkaz:
http://arxiv.org/abs/2402.03732
Autor:
Febrinanto, Falih Gozi, Moore, Kristen, Thapa, Chandra, Liu, Mujie, Saikrishna, Vidya, Ma, Jiangang, Xia, Feng
Many multivariate time series anomaly detection frameworks have been proposed and widely applied. However, most of these frameworks do not consider intrinsic relationships between variables in multivariate time series data, thus ignoring the causal r
Externí odkaz:
http://arxiv.org/abs/2312.09478
This study delves into the capabilities and limitations of Large Language Models (LLMs) in the challenging domain of conditional question-answering. Utilizing the Conditional Question Answering (CQA) dataset and focusing on generative models like T5
Externí odkaz:
http://arxiv.org/abs/2312.01143
Publikováno v:
Journal of Clinical and Diagnostic Research, Vol 18, Iss 10, Pp 04-07 (2024)
Uncommon spontaneous haematomas of the iliacus muscle are observed in patients on anticoagulant medication or in those with blood dyscrasias like haemophilia. Femoral neuropathy, which may involve pain and paralysis, can arise as a result of these ha
Externí odkaz:
https://doaj.org/article/ff3fb3d2fd944b10abd38c4b807dcf74
Autor:
Wu, Yijing, Rallabandi, SaiKrishna, Srinivasamurthy, Ravisutha, Dakle, Parag Pravin, Gon, Alolika, Raghavan, Preethi
Spoken question answering (SQA) systems are critical for digital assistants and other real-world use cases, but evaluating their performance is a challenge due to the importance of human-spoken questions. This study presents a new large-scale communi
Externí odkaz:
http://arxiv.org/abs/2304.13689