Zobrazeno 1 - 10
of 29 750
pro vyhledávání: '"Vipin IS"'
Supernovae represent some of the most energetically explosive events in the universe, with a substantial fraction of their released gravitational energy carried away by neutrinos. This study evaluates the sensitivity of three next-generation neutrino
Externí odkaz:
http://arxiv.org/abs/2411.07716
Autor:
Hutchings, Adam, Yarnot, Eric, Li, Xinpeng, Guan, Qiang, Xie, Ning, Xu, Shuai, Chaudhary, Vipin
The variational quantum eigensolver (VQE), a type of variational quantum algorithm, is a hybrid quantum-classical algorithm to find the lowest-energy eigenstate of a particular Hamiltonian. We investigate ways to optimize the VQE solving process on m
Externí odkaz:
http://arxiv.org/abs/2410.21413
Autor:
Li, Xinpeng, Kulkarni, Vinooth, Chen, Daniel T., Guan, Qiang, Jiang, Weiwen, Xie, Ning, Xu, Shuai, Chaudhary, Vipin
Current quantum devices face challenges when dealing with large circuits due to error rates as circuit size and the number of qubits increase. The circuit wire-cutting technique addresses this issue by breaking down a large circuit into smaller, more
Externí odkaz:
http://arxiv.org/abs/2410.20313
Autor:
Gunda, Vipin, Mehta, Archit
We introduce a modified Weighted Cascade model that integrates asymmetric budgets and product scores, providing new insights into the Generalized Asymmetric Influence Maximization problem, which we establish as NP-hard. Our simulations demonstrate th
Externí odkaz:
http://arxiv.org/abs/2411.03335
The prediction of streamflows and other environmental variables in unmonitored basins is a grand challenge in hydrology. Recent machine learning (ML) models can harness vast datasets for accurate predictions at large spatial scales. However, there ar
Externí odkaz:
http://arxiv.org/abs/2410.19865
Autor:
Xu, Shaoming, Renganathan, Arvind, Khandelwal, Ankush, Ghosh, Rahul, Li, Xiang, Liu, Licheng, Tayal, Kshitij, Harrington, Peter, Jia, Xiaowei, Jin, Zhenong, Nieber, Jonh, Kumar, Vipin
Streamflow, vital for water resource management, is governed by complex hydrological systems involving intermediate processes driven by meteorological forces. While deep learning models have achieved state-of-the-art results of streamflow prediction,
Externí odkaz:
http://arxiv.org/abs/2410.14137
Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes. Traditional time series approaches for prediction often focus on either autoregressive modeling, which relies solely on past obse
Externí odkaz:
http://arxiv.org/abs/2410.12184
Autor:
Wang, Guanchu, Chuang, Yu-Neng, Tang, Ruixiang, Zhong, Shaochen, Yuan, Jiayi, Jin, Hongye, Liu, Zirui, Chaudhary, Vipin, Xu, Shuai, Caverlee, James, Hu, Xia
Ensuring the security of released large language models (LLMs) poses a significant dilemma, as existing mechanisms either compromise ownership rights or raise data privacy concerns. To address this dilemma, we introduce TaylorMLP to protect the owner
Externí odkaz:
http://arxiv.org/abs/2410.05331
Generative models can now produce photorealistic synthetic data which is virtually indistinguishable from the real data used to train it. This is a significant evolution over previous models which could produce reasonable facsimiles of the training d
Externí odkaz:
http://arxiv.org/abs/2410.01322
Deep neural networks (DNNs), while increasingly deployed in many applications, struggle with robustness against anomalous and out-of-distribution (OOD) data. Current OOD benchmarks often oversimplify, focusing on single-object tasks and not fully rep
Externí odkaz:
http://arxiv.org/abs/2409.18235