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of 2 064
pro vyhledávání: '"P, Sirohi"'
With the increase of data in day-to-day life, businesses and different stakeholders need to analyze the data for better predictions. Traditionally, relational data has been a source of various insights, but with the increase in computational power an
Externí odkaz:
http://arxiv.org/abs/2409.19096
Autor:
Concha, David Molina, Li, Jiping, Yin, Haoran, Park, Kyeonghyeon, Lee, Hyun-Rok, Lee, Taesik, Sirohi, Dhruv, Lee, Chi-Guhn
This study addresses the challenge of fleet design optimization in the context of heterogeneous multi-robot fleets, aiming to obtain feasible designs that balance performance and costs. In the domain of autonomous multi-robot exploration, reinforceme
Externí odkaz:
http://arxiv.org/abs/2408.11751
Autor:
Prasanna, Sai, Honerkamp, Daniel, Sirohi, Kshitij, Welschehold, Tim, Burgard, Wolfram, Valada, Abhinav
Publikováno v:
Proceedings of the International Symposium on Robotics Research (ISRR), 2024
Embodied AI has made significant progress acting in unexplored environments. However, tasks such as object search have largely focused on efficient policy learning. In this work, we identify several gaps in current search methods: They largely focus
Externí odkaz:
http://arxiv.org/abs/2408.02297
Landmark judgments are of prime importance in the Common Law System because of their exceptional jurisprudence and frequent references in other judgments. In this work, we leverage contextual references available in citing judgments to create an extr
Externí odkaz:
http://arxiv.org/abs/2406.10824
We address the growing apprehension that GNNs, in the absence of fairness constraints, might produce biased decisions that disproportionately affect underprivileged groups or individuals. Departing from previous work, we introduce for the first time
Externí odkaz:
http://arxiv.org/abs/2402.12937
The availability of a robust map-based localization system is essential for the operation of many autonomously navigating vehicles. Since uncertainty is an inevitable part of perception, it is beneficial for the robustness of the robot to consider it
Externí odkaz:
http://arxiv.org/abs/2402.05840
Autor:
Zhang, Bodong, Manoochehri, Hamid, Ho, Man Minh, Fooladgar, Fahimeh, Chong, Yosep, Knudsen, Beatrice S., Sirohi, Deepika, Tasdizen, Tolga
Histopathological image classification is an important task in medical image analysis. Recent approaches generally rely on weakly supervised learning due to the ease of acquiring case-level labels from pathology reports. However, patch-level classifi
Externí odkaz:
http://arxiv.org/abs/2312.06978
Ensuring fairness in Recommendation Systems (RSs) across demographic groups is critical due to the increased integration of RSs in applications such as personalized healthcare, finance, and e-commerce. Graph-based RSs play a crucial role in capturing
Externí odkaz:
http://arxiv.org/abs/2312.10080
Publikováno v:
Indian Journal of Paediatric Dermatology, Vol 25, Iss 3, Pp 195-200 (2024)
Background: Localized involutional lipoatrophy (LIL) is a rare distinctive idiopathic form of localized lipoatrophy. It is characterized by loss of adipose tissue without antecedent inflammation and was first described by Peters and Winkelmann in 198
Externí odkaz:
https://doaj.org/article/e11621000e514a08a917965d1d1d20f2
Autor:
Nallapareddy, Monish R., Sirohi, Kshitij, Drews-Jr, Paulo L. J., Burgard, Wolfram, Cheng, Chih-Hong, Valada, Abhinav
Uncertainty estimation is crucial in safety-critical settings such as automated driving as it provides valuable information for several downstream tasks including high-level decision making and path planning. In this work, we propose EvCenterNet, a n
Externí odkaz:
http://arxiv.org/abs/2303.03037