Zobrazeno 1 - 10
of 3 826
pro vyhledávání: '"Deren, P."'
Autor:
Xu, Rui, Mengya, Hu, Lei, Deren, Li, Yaxi, Lowe, David, Gorevski, Alex, Wang, Mingyu, Ching, Emily, Deng, Alex
The proliferation of AI-generated images has intensified the need for robust content authentication methods. We present InvisMark, a novel watermarking technique designed for high-resolution AI-generated images. Our approach leverages advanced neural
Externí odkaz:
http://arxiv.org/abs/2411.07795
Autor:
Ferrante, Laura, Boesendorfer, Anna, Barsakcioglu, Deren Yusuf, Baumgartner, Benedikt, Al-Ajam, Yazan, Woollard, Alex, Kang, Norbert Venantius, Aszmann, Oskar, Farina, Dario
Following limb amputation, neural signals for limb functions persist in the residual peripheral nerves. Targeted muscle reinnervation (TMR) allows to redirected these signals into spare muscles to recover the neural information through electromyograp
Externí odkaz:
http://arxiv.org/abs/2410.10694
Autor:
Han, Deren, Xie, Jiaxin
The Kaczmarz method and its variants, which are types of stochastic gradient descent (SGD) methods, have been extensively studied for their simplicity and efficiency in solving linear systems. Random reshuffling (RR), also known as SGD without replac
Externí odkaz:
http://arxiv.org/abs/2410.01140
Autor:
Yan, Yuchao, Liu, Yingying, Wang, Ziyi, Liu, Da, Gao, Xu, Wang, Yan, Li, Cheng, Ma, KeKe, Xia, Ning, Jin, Zhu, Deng, Tianqi, Zhang, Hui, Yang, Deren
Grain boundaries have extensive influence on the performance of crystal materials. However, the atomic-scale structure and its relation with local and crystallographic symmetries remain elusive in low-symmetry crystals. Herein, we find that the local
Externí odkaz:
http://arxiv.org/abs/2409.14681
This paper presents an Accelerated Preconditioned Proximal Gradient Algorithm (APPGA) for effectively solving a class of Positron Emission Tomography (PET) image reconstruction models with differentiable regularizers. We establish the convergence of
Externí odkaz:
http://arxiv.org/abs/2409.13344
Autor:
Hu, Mengya, Xu, Rui, Lei, Deren, Li, Yaxi, Wang, Mingyu, Ching, Emily, Kamal, Eslam, Deng, Alex
Large language models (LLMs) are highly capable but face latency challenges in real-time applications, such as conducting online hallucination detection. To overcome this issue, we propose a novel framework that leverages a small language model (SLM)
Externí odkaz:
http://arxiv.org/abs/2408.12748
This paper proposes LCFL, a novel clustering metric for evaluating clients' data distributions in federated learning. LCFL aligns with federated learning requirements, accurately assessing client-to-client variations in data distribution. It offers a
Externí odkaz:
http://arxiv.org/abs/2407.09360
Alternating structure-adapted proximal (ASAP) gradient algorithm (M. Nikolova and P. Tan, SIAM J Optim, 29:2053-2078, 2019) has drawn much attention due to its efficiency in solving nonconvex nonsmooth optimization problems. However, the multiblock n
Externí odkaz:
http://arxiv.org/abs/2406.17226
In this paper, we propose a novel adaptive stochastic extended iterative method, which can be viewed as an improved extension of the randomized extended Kaczmarz (REK) method, for finding the unique minimum Euclidean norm least-squares solution of a
Externí odkaz:
http://arxiv.org/abs/2405.19044
Randomized iterative methods, such as the Kaczmarz method and its variants, have gained growing attention due to their simplicity and efficiency in solving large-scale linear systems. Meanwhile, absolute value equations (AVE) have attracted increasin
Externí odkaz:
http://arxiv.org/abs/2405.04091