Zobrazeno 1 - 10
of 20 798
pro vyhledávání: '"P. Chaudhuri"'
We explore the dynamics of active elements performing persistent random motion with fluctuating active speed and in the presence of translational noise in a $d$-dimensional harmonic trap, modeling active speed generation through an Ornstein-Uhlenbeck
Externí odkaz:
http://arxiv.org/abs/2410.22004
Empirical auditing has emerged as a means of catching some of the flaws in the implementation of privacy-preserving algorithms. Existing auditing mechanisms, however, are either computationally inefficient requiring multiple runs of the machine learn
Externí odkaz:
http://arxiv.org/abs/2410.22235
Autor:
Aliakbarpour, Maryam, Chaudhuri, Syomantak, Courtade, Thomas A., Fallah, Alireza, Jordan, Michael I.
Local Differential Privacy (LDP) offers strong privacy guarantees without requiring users to trust external parties. However, LDP applies uniform protection to all data features, including less sensitive ones, which degrades performance of downstream
Externí odkaz:
http://arxiv.org/abs/2410.18404
Systems Biology Graphical Notation (SBGN) is a standardised notational system that visualises biochemical processes as networks. These visualizations lack a formal framework, so that the analysis of such networks through modelling and simulation is a
Externí odkaz:
http://arxiv.org/abs/2410.18024
Autor:
Chaudhuri, Jayeeta, Nassar, Hassan, Gnad, Dennis R. E., Henkel, Jorg, Tahoori, Mehdi B., Chakrabarty, Krishnendu
FPGAs are now ubiquitous in cloud computing infrastructures and reconfigurable system-on-chip, particularly for AI acceleration. Major cloud service providers such as Amazon and Microsoft are increasingly incorporating FPGAs for specialized compute-i
Externí odkaz:
http://arxiv.org/abs/2410.16497
Autor:
Rittler, Nick, Chaudhuri, Kamalika
Generative models at times produce "invalid" outputs, such as images with generation artifacts and unnatural sounds. Validity-constrained distribution learning attempts to address this problem by requiring that the learned distribution have a provabl
Externí odkaz:
http://arxiv.org/abs/2410.16253
Machine unlearning is a key requirement of many data protection regulations such as GDPR. Prior work on unlearning has mostly considered superficial unlearning tasks where a single or a few related pieces of information are required to be removed. Ho
Externí odkaz:
http://arxiv.org/abs/2410.15153
Autor:
Xing, Junjie, He, Yeye, Zhou, Mengyu, Dong, Haoyu, Han, Shi, Zhang, Dongmei, Chaudhuri, Surajit
In this work, we propose Table-LLM-Specialist, or Table-Specialist for short, as a new self-trained fine-tuning paradigm specifically designed for table tasks. Our insight is that for each table task, there often exist two dual versions of the same t
Externí odkaz:
http://arxiv.org/abs/2410.12164
Autor:
Haldar, Asmi, Das, Anirban, Chaudhuri, Sagnik, Staszewski, Luke, Wietek, Alexander, Pollmann, Frank, Moessner, Roderich, Das, Arnab
The ergodicity postulate, a foundational pillar of Gibbsian statistical mechanics predicts that a periodically driven (Floquet) system in the absence of any conservation law heats to a featureless `infinite temperature' state. Here, we find--for a cl
Externí odkaz:
http://arxiv.org/abs/2410.11050
Large language models (LLMs) are becoming increasingly prevalent in modern software systems, interfacing between the user and the internet to assist with tasks that require advanced language understanding. To accomplish these tasks, the LLM often use
Externí odkaz:
http://arxiv.org/abs/2410.05451