Zobrazeno 1 - 10
of 44 599
pro vyhledávání: '"Rastogi, A"'
In response to the unique challenges of Mixed Reality (MR) game development, we developed GAMR, an analytics tool specifically designed for MR games. GAMR aims to assist developers in identifying and resolving gameplay issues effectively. It features
Externí odkaz:
http://arxiv.org/abs/2408.01573
Parameter-efficient fine-tuning techniques like Low-Rank Adaptation (LoRA) have revolutionized the adaptation of large language models (LLMs) to diverse tasks. Recent efforts have explored mixtures of LoRA modules for multi-task settings. However, ou
Externí odkaz:
http://arxiv.org/abs/2408.01505
Autor:
Rastogi, Shubham, U, Vijaya Kumar
We find the commutant of a pure contractive semigroup on a Hilbert space. We demonstrate that any tuple of doubly commuting pure contractive semigroups can be dilated to a tuple of doubly commuting pure isometric semigroups. En route, we obtain a com
Externí odkaz:
http://arxiv.org/abs/2407.19935
Cyber threats are constantly evolving. Extracting actionable insights from unstructured Cyber Threat Intelligence (CTI) data is essential to guide cybersecurity decisions. Increasingly, organizations like Microsoft, Trend Micro, and CrowdStrike are u
Externí odkaz:
http://arxiv.org/abs/2407.02528
Autor:
Kungurtsev, Vyacheslav, Apaar, Khandelwal, Aarya, Rastogi, Parth Sandeep, Chatterjee, Bapi, Mareček, Jakub
In this work, we demonstrate the Empirical Bayes approach to learning a Dynamic Bayesian Network. By starting with several point estimates of structure and weights, we can use a data-driven prior to subsequently obtain a model to quantify uncertainty
Externí odkaz:
http://arxiv.org/abs/2406.17831
Cyber threat intelligence (CTI) is crucial in today's cybersecurity landscape, providing essential insights to understand and mitigate the ever-evolving cyber threats. The recent rise of Large Language Models (LLMs) have shown potential in this domai
Externí odkaz:
http://arxiv.org/abs/2406.07599
Autor:
Luo, Liangchen, Liu, Yinxiao, Liu, Rosanne, Phatale, Samrat, Lara, Harsh, Li, Yunxuan, Shu, Lei, Zhu, Yun, Meng, Lei, Sun, Jiao, Rastogi, Abhinav
Complex multi-step reasoning tasks, such as solving mathematical problems or generating code, remain a significant hurdle for even the most advanced large language models (LLMs). Verifying LLM outputs with an Outcome Reward Model (ORM) is a standard
Externí odkaz:
http://arxiv.org/abs/2406.06592
We consider holomorphic functions on the unit disc whose images are contained in a strip of the complex plane. Under an additional condition, such functions are constants. We also consider appropriate operator valued versions. Applications are found
Externí odkaz:
http://arxiv.org/abs/2406.02901
Autor:
Dennler, Nik, Drix, Damien, Warner, Tom P. A., Rastogi, Shavika, Della Casa, Cecilia, Ackels, Tobias, Schaefer, Andreas T., van Schaik, André, Schmuker, Michael
Animals have evolved to rapidly detect and recognise brief and intermittent encounters with odour packages, exhibiting recognition capabilities within milliseconds. Artificial olfaction has faced challenges in achieving comparable results -- existing
Externí odkaz:
http://arxiv.org/abs/2406.01904
In this paper, we present a multidimensional, highly effective method for aggregating data for wireless sensor networks while maintaining privacy. The suggested system is resistant to data loss and secure against both active and passive privacy compr
Externí odkaz:
http://arxiv.org/abs/2403.20120