Zobrazeno 1 - 10
of 4 337
pro vyhledávání: '"A. Ayyagari"'
Diagnostic errors in healthcare persist as a critical challenge, with increasing numbers of patients turning to online resources for health information. While AI-powered healthcare chatbots show promise, there exists no standardized and scalable fram
Externí odkaz:
http://arxiv.org/abs/2412.12538
Autor:
Dukkipati, Ambedkar, Ayyagari, Ranga Shaarad, Dasgupta, Bodhisattwa, Dutta, Parag, Onteru, Prabhas Reddy
Learning agents that excel at sequential decision-making tasks must continuously resolve the problem of exploration and exploitation for optimal learning. However, such interactions with the environment online might be prohibitively expensive and may
Externí odkaz:
http://arxiv.org/abs/2412.13106
Standard reinforcement learning algorithms with a single policy perform poorly on tasks in complex environments involving sparse rewards, diverse behaviors, or long-term planning. This led to the study of algorithms that incorporate temporal abstract
Externí odkaz:
http://arxiv.org/abs/2407.15241
Autor:
Niculescu, Gabriela E., Bejger, Gerald R., Barber, John P., Wright, Joshua T., Almishal, Saeed S. I., Webb, Matthew, Ayyagari, Sai Venkata Gayathri, Maria, Jon-Paul, Alem, Nasim, Heron, John T., Rost, Christina M.
High entropy oxides (HEO)s have garnered much interest due to their available high degree of tunability. Here, we study the local structure of (MgNiCuCoZn)0.167(MnCr)0.083O, a composition based on the parent HEO (MgNiCuCoZn)0.2O.We synthesized a seri
Externí odkaz:
http://arxiv.org/abs/2406.13550
Last-layer retraining methods have emerged as an efficient framework for correcting existing base models. Within this framework, several methods have been proposed to deal with correcting models for subgroup fairness with and without group membership
Externí odkaz:
http://arxiv.org/abs/2406.09561
Autor:
Stromberg, Nathan, Ayyagari, Rohan, Welfert, Monica, Koyejo, Sanmi, Nock, Richard, Sankar, Lalitha
Existing methods for last layer retraining that aim to optimize worst-group accuracy (WGA) rely heavily on well-annotated groups in the training data. We show, both in theory and practice, that annotation-based data augmentations using either downsam
Externí odkaz:
http://arxiv.org/abs/2402.11039
Autor:
Ayyagari, Shalini R., author
Publikováno v:
Music and Dance as Everyday South Asia, 2024.
Externí odkaz:
https://doi.org/10.1093/oso/9780197566237.003.0006
Publikováno v:
2022
Here, we explore the different characteristics of a possible coupling between tropospheric and ionospheric activities during the impact of tropical cyclones (TC) like Amphan and Nisarga in the Indian subcontinent. We have analyzed the effect of TCs A
Externí odkaz:
http://arxiv.org/abs/2310.18114
Publikováno v:
Journal of Structural Fire Engineering, 2023, Vol. 15, Issue 3, pp. 338-361.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JSFE-04-2023-0025
Most reinforcement learning algorithms treat the context under which they operate as a stationary, isolated, and undisturbed environment. However, in real world applications, environments constantly change due to a variety of external events. To addr
Externí odkaz:
http://arxiv.org/abs/2305.16056