Zobrazeno 1 - 10
of 18 031
pro vyhledávání: '"P., Naresh"'
Autor:
Rawte, Vipula, Jain, Sarthak, Sinha, Aarush, Kaushik, Garv, Bansal, Aman, Vishwanath, Prathiksha Rumale, Jain, Samyak Rajesh, Reganti, Aishwarya Naresh, Jain, Vinija, Chadha, Aman, Sheth, Amit P., Das, Amitava
Latest developments in Large Multimodal Models (LMMs) have broadened their capabilities to include video understanding. Specifically, Text-to-video (T2V) models have made significant progress in quality, comprehension, and duration, excelling at crea
Externí odkaz:
http://arxiv.org/abs/2411.10867
In the usual statistical inference problem, we estimate an unknown parameter of a statistical model using the information in the random sample. A priori information about the parameter is also known in several real-life situations. One such informati
Externí odkaz:
http://arxiv.org/abs/2411.05487
Autor:
Dadhich, Naresh, Goswami, Rituparno
Publikováno v:
Phys. Rev. D 110, L101501 (2024)
It is well known that locally defined marginally outer trapped surface (MOTS) is null and coincident with the event horizon of an unperturbed static Schwarzschild black hole. This is however not true for an accreting black hole for which MOTS separat
Externí odkaz:
http://arxiv.org/abs/2411.00424
Autor:
Gundla, Naresh Kumar
In the contemporary world of dynamic digital solutions and services, the significance of effective and stable cloud solutions cannot be overestimated. The cloud adaptation is becoming more popular due to mobile advantages, including flexibility, chea
Externí odkaz:
http://arxiv.org/abs/2410.21740
The use of social media applications, hate speech engagement, and public debates among teenagers, primarily by university and college students, is growing day by day. The feelings of tremendous stress, anxiety, and depression via social media among o
Externí odkaz:
http://arxiv.org/abs/2410.20070
The financial sector's dependence on digital infrastructure increases its vulnerability to cybersecurity threats, requiring strong IT security protocols with other entities. This collaboration, however, is often identified as the most vulnerable link
Externí odkaz:
http://arxiv.org/abs/2410.15194
Automating end-to-end Exploratory Data Analysis (AutoEDA) is a challenging open problem, often tackled through Reinforcement Learning (RL) by learning to predict a sequence of analysis operations (FILTER, GROUP, etc). Defining rewards for each operat
Externí odkaz:
http://arxiv.org/abs/2410.11276
Robustness towards adversarial attacks is a vital property for classifiers in several applications such as autonomous driving, medical diagnosis, etc. Also, in such scenarios, where the cost of misclassification is very high, knowing when to abstain
Externí odkaz:
http://arxiv.org/abs/2410.10736
Autor:
Sundaresha, Vignesh, Shanbhag, Naresh
The ubiquitous deployment of deep learning systems on resource-constrained Edge devices is hindered by their high computational complexity coupled with their fragility to out-of-distribution (OOD) data, especially to naturally occurring common corrup
Externí odkaz:
http://arxiv.org/abs/2410.07691
In the current legal environment, it is essential to prioritize the protection and reliability of data to promote trust and effectiveness. This study examines how blockchain technology in the form of blockLAW can be applicable to investigate its effe
Externí odkaz:
http://arxiv.org/abs/2410.06143