Zobrazeno 1 - 10
of 5 572
pro vyhledávání: '"Lokhande P"'
Deep neural networks (DNNs) offer plenty of challenges in executing efficient computation at edge nodes, primarily due to the huge hardware resource demands. The article proposes HYDRA, hybrid data multiplexing, and runtime layer configurable DNN acc
Externí odkaz:
http://arxiv.org/abs/2409.04976
Publikováno v:
28th International Symposium on VLSI Design and Test (VDAT 2024)
This paper presents the Hybrid Overestimating Approximate Adder designed to enhance the performance in processing engines, specifically focused on edge AI applications. A novel Plus One Adder design is proposed as an incremental adder in the RCA chai
Externí odkaz:
http://arxiv.org/abs/2408.00806
Prompt Tuning has emerged as a prominent research paradigm for adapting vision-language models to various downstream tasks. However, recent research indicates that prompt tuning methods often lead to overfitting due to limited training samples. In th
Externí odkaz:
http://arxiv.org/abs/2407.15894
Text-driven image synthesis has made significant advancements with the development of diffusion models, transforming how visual content is generated from text prompts. Despite these advances, text-driven image editing, a key area in computer graphics
Externí odkaz:
http://arxiv.org/abs/2406.00985
Small sample sizes are common in many disciplines, which necessitates pooling roughly similar datasets across multiple institutions to study weak but relevant associations between images and disease outcomes. Such data often manifest shift/imbalance
Externí odkaz:
http://arxiv.org/abs/2403.02598
This compilation of various research paper highlights provides a comprehensive overview of recent developments in super-resolution image and video using deep learning algorithms such as Generative Adversarial Networks. The studies covered in these su
Externí odkaz:
http://arxiv.org/abs/2312.16471
Autor:
Kumbhar, Arti, Chougule, Amruta, Lokhande, Priya, Navaghane, Saloni, Burud, Aditi, Nimbalkar, Saee
Utilizing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), our system introduces an innovative approach to defect detection in manufacturing. This technology excels in precisely ident
Externí odkaz:
http://arxiv.org/abs/2311.03725
Autor:
Miranda-Quintana, Ramón Alain, Kim, Taewon D., Lokhande, Rugwed A., Richer, M., Sánchez-Díaz, Gabriela, Gaikwad, Pratiksha B., Ayers, Paul W.
We propose a new Perturbation Theory framework that can be used to help with the projective solution of the Schr\"odinger equation for arbitrary wavefunctions. This Flexible Ansatz for N-body Perturbation Theory (FANPT) is based on our previously pro
Externí odkaz:
http://arxiv.org/abs/2310.03096
Autor:
Gaikwad, Pratiksha B., Kim, Taewon D., Richer, M., Lokhande, Rugwed A., Sánchez-Díaz, Gabriela, Limacher, Peter A., Ayers, Paul W., Miranda-Quintana, Ramón Alain
Electron pairs have an illustrious history in chemistry, from powerful concepts to understanding structural stability and reactive changes, to the promise of serving as building blocks of quantitative descriptions of the electronic structure of compl
Externí odkaz:
http://arxiv.org/abs/2310.01764
Autor:
Dinesh Parshuram Satpute, Garvita Narang, Harshal Rohit, Jagdish Manjhi, Divita Kumar, Sangita Dattatray Shinde, Shyam Kumar Lokhande, Priyanka Patel Vatsa, Vinal Upadhyay, Shivkanya Madhavrao Bhujbal, Amit Mandoli, Dinesh Kumar
Publikováno v:
JACS Au, Vol 4, Iss 11, Pp 4474-4487 (2024)
Externí odkaz:
https://doaj.org/article/e2f599dd9916434f9cba8f502ca48ae7
