Zobrazeno 1 - 10
of 4 240
pro vyhledávání: '"A Ayyagari"'
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
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
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
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
Autor:
Wu, Yuting, Wang, Qiwen, Wang, Ziyu, Wang, Xinxin, Ayyagari, Buvna, Krishnan, Siddarth, Chudzik, Michael, Lu, Wei D.
Publikováno v:
Adv. Mater.35 (2023) 2305465
The need for deep neural network (DNN) models with higher performance and better functionality leads to the proliferation of very large models. Model training, however, requires intensive computation time and energy. Memristor-based compute-in-memory
Externí odkaz:
http://arxiv.org/abs/2305.14547
Autor:
Min, Lujin, Zhang, Yang, Xie, Zhijian, Ayyagari, Sai Venkata Gayathri, Miao, Leixin, Onishi, Yugo, Lee, Seng Huat, Wang, Yu, Alem, Nasim, Fu, Liang, Mao, Zhiqiang
Nonreciprocal (NR) charge transport in quantum materials has attracted enormous interest since it offers an avenue to investigate quantum symmetry related physics and holds many prospective applications such as rectification and photodetection over a
Externí odkaz:
http://arxiv.org/abs/2303.03738
Publikováno v:
Lecture Notes in Networks and Systems (2021)
Intense geomagnetic storms can have a strong impact on the signals (termed ionospheric scintillations) emitted by any global navigation satellite system (GNSS). The paper reports the first studies of scintillations at the Indore region on the NavIC s
Externí odkaz:
http://arxiv.org/abs/2302.06181