Zobrazeno 1 - 10
of 31 620
pro vyhledávání: '"Sudarshan,"'
Non-local systems of conservation laws play a crucial role in modeling flow mechanisms across various scenarios. The well-posedness of such problems is typically established by demonstrating the convergence of robust first-order schemes. However, ach
Externí odkaz:
http://arxiv.org/abs/2412.18475
Autor:
Svanidze, Anastasiia, Kundu, Sudarshan, Iadlovska, Olena, Thakur, Anil K., Zheng, Xiaoyu, Palffy-Muhoray, Peter
Azo-containing liquid crystal elatomers are photomechanical materials which can be actuated by illumination. The photomechanical response is a result of the photoisomerization of the azo moiety, which produces bulk stresses in the material. These str
Externí odkaz:
http://arxiv.org/abs/2412.05791
All-weather image restoration (AWIR) is crucial for reliable autonomous navigation under adverse weather conditions. AWIR models are trained to address a specific set of weather conditions such as fog, rain, and snow. But this causes them to often st
Externí odkaz:
http://arxiv.org/abs/2411.17814
Deep learning-based models for All-In-One Image Restoration (AIOR) have achieved significant advancements in recent years. However, their practical applicability is limited by poor generalization to samples outside the training distribution. This lim
Externí odkaz:
http://arxiv.org/abs/2411.17687
We propose a new approach for fine-grained uncertainty quantification (UQ) using a collision matrix. For a classification problem involving $K$ classes, the $K\times K$ collision matrix $S$ measures the inherent (aleatoric) difficulty in distinguishi
Externí odkaz:
http://arxiv.org/abs/2411.12127
Autor:
Regmi, Sudarshan
Out-of-distribution (OOD) detection is critical to ensure the safe deployment of deep learning models in critical applications. Deep learning models can often misidentify OOD samples as in-distribution (ID) samples. This vulnerability worsens in the
Externí odkaz:
http://arxiv.org/abs/2411.10794
Autor:
Kang, Beomseok, Saha, Priyabrata, Sharma, Sudarshan, Chakraborty, Biswadeep, Mukhopadhyay, Saibal
We introduce a novel framework, Online Relational Inference (ORI), designed to efficiently identify hidden interaction graphs in evolving multi-agent interacting systems using streaming data. Unlike traditional offline methods that rely on a fixed tr
Externí odkaz:
http://arxiv.org/abs/2411.01442
Autor:
Ananth, Sudarshan, Bhave, Nipun
We construct maximal supergravity in five-dimensions by 'oxidizing' the four-dimensional $\mathcal{N}=8$ theory. The relevant symmetries, the unitary symplectic group $USp(8)$ and the exceptional group $E_6$, are both presented in light-cone superspa
Externí odkaz:
http://arxiv.org/abs/2410.19463
This position paper proposes a novel approach to advancing NLP security by leveraging Large Language Models (LLMs) as engines for generating diverse adversarial attacks. Building upon recent work demonstrating LLMs' effectiveness in creating word-lev
Externí odkaz:
http://arxiv.org/abs/2410.18215