Zobrazeno 1 - 10
of 10 647
pro vyhledávání: '"A, Nambiar"'
In the past couple of decades, there have been significant advances in measuring quantum properties of light, such as quadratures of squeezed light and single-photon counting. Here, we explore whether such tools can be leveraged to probe electronic c
Externí odkaz:
http://arxiv.org/abs/2410.24215
Sonar image synthesis is crucial for advancing applications in underwater exploration, marine biology, and defence. Traditional methods often rely on extensive and costly data collection using sonar sensors, jeopardizing data quality and diversity. T
Externí odkaz:
http://arxiv.org/abs/2410.08612
Autor:
Feuer, Benjamin, Goldblum, Micah, Datta, Teresa, Nambiar, Sanjana, Besaleli, Raz, Dooley, Samuel, Cembalest, Max, Dickerson, John P.
The release of ChatGPT in November 2022 sparked an explosion of interest in post-training and an avalanche of new preference optimization (PO) methods. These methods claim superior alignment by virtue of better correspondence with human pairwise pref
Externí odkaz:
http://arxiv.org/abs/2409.15268
Deep learning techniques have revolutionized image classification by mimicking human cognition and automating complex decision-making processes. However, the deployment of AI systems in the wild, especially in high-security domains such as defence, i
Externí odkaz:
http://arxiv.org/abs/2408.12837
Autor:
S, Kamal Basha, Nambiar, Athira
Acoustic sonar imaging systems are widely used for underwater surveillance in both civilian and military sectors. However, acquiring high-quality sonar datasets for training Artificial Intelligence (AI) models confronts challenges such as limited dat
Externí odkaz:
http://arxiv.org/abs/2408.12833
Deep Neural Networks (DNNs) have revolutionized various fields by enabling task automation and reducing human error. However, their internal workings and decision-making processes remain obscure due to their black box nature. Consequently, the lack o
Externí odkaz:
http://arxiv.org/abs/2408.12808
Autor:
Mandalika, Sriram, Nambiar, Athira
Most of the sophisticated AI models utilize huge amounts of annotated data and heavy training to achieve high-end performance. However, there are certain challenges that hinder the deployment of AI models "in-the-wild" scenarios, i.e., inefficient us
Externí odkaz:
http://arxiv.org/abs/2408.04482
The use of large language models (LLMs) is expanding rapidly, and open-source versions are becoming available, offering users safer and more adaptable options. These models enable users to protect data privacy by eliminating the need to provide data
Externí odkaz:
http://arxiv.org/abs/2408.02201
Autor:
Rad, Ali, Schuckert, Alexander, Crane, Eleanor, Nambiar, Gautam, Fei, Fan, Wyrick, Jonathan, Silver, Richard M., Hafezi, Mohammad, Davoudi, Zohreh, Gullans, Michael J.
Simulating fermions coupled to spin degrees of freedom, relevant for a range of quantum field theories, represents a promising application for quantum simulators. Mapping fermions to qubits is challenging in $2+1$ and higher spacetime dimensions, and
Externí odkaz:
http://arxiv.org/abs/2407.03419
Autor:
Levillain, Jessie, Alouges, François, Desimone, Antonio, Choudhary, Akash, Nambiar, Sankalp, Bochert, Ida
It has been recently shown that it is possible to design simple artificial swimmers at low Reynoldsnumber that possess only one degree of freedom and, nevertheless, can overcome Purcell's celebratedscallop theorem. One of the few examples is given by
Externí odkaz:
http://arxiv.org/abs/2403.10556