Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Alkhouri, Ismail"'
Autor:
Alkhouri, Ismail, Denmat, Cedric Le, Li, Yingjie, Yu, Cunxi, Liu, Jia, Wang, Rongrong, Velasquez, Alvaro
Combinatorial Optimization (CO) plays a crucial role in addressing various significant problems, among them the challenging Maximum Independent Set (MIS) problem. In light of recent advancements in deep learning methods, efforts have been directed to
Externí odkaz:
http://arxiv.org/abs/2406.19532
Deep Neural Networks (DNNs) have achieved remarkable success in addressing many previously unsolvable tasks. However, the storage and computational requirements associated with DNNs pose a challenge for deploying these trained models on resource-limi
Externí odkaz:
http://arxiv.org/abs/2405.03089
Diffusion models have recently gained traction as a powerful class of deep generative priors, excelling in a wide range of image restoration tasks due to their exceptional ability to model data distributions. To solve image restoration problems, many
Externí odkaz:
http://arxiv.org/abs/2403.06054
Diffusion models, emerging as powerful deep generative tools, excel in various applications. They operate through a two-steps process: introducing noise into training samples and then employing a model to convert random noise into new samples (e.g.,
Externí odkaz:
http://arxiv.org/abs/2312.09181
Autor:
Liang, Shijun, Nguyen, Van Hoang Minh, Jia, Jinghan, Alkhouri, Ismail, Liu, Sijia, Ravishankar, Saiprasad
As the popularity of deep learning (DL) in the field of magnetic resonance imaging (MRI) continues to rise, recent research has indicated that DL-based MRI reconstruction models might be excessively sensitive to minor input disturbances, including wo
Externí odkaz:
http://arxiv.org/abs/2312.07784
Deep learning (DL) techniques have been extensively employed in magnetic resonance imaging (MRI) reconstruction, delivering notable performance enhancements over traditional non-DL methods. Nonetheless, recent studies have identified vulnerabilities
Externí odkaz:
http://arxiv.org/abs/2309.05794
Autor:
Alkhouri, Ismail, Jha, Sumit, Beckus, Andre, Atia, George, Velasquez, Alvaro, Ewetz, Rickard, Ramanathan, Arvind, Jha, Susmit
Protein folding neural networks (PFNNs) such as AlphaFold predict remarkably accurate structures of proteins compared to other approaches. However, the robustness of such networks has heretofore not been explored. This is particularly relevant given
Externí odkaz:
http://arxiv.org/abs/2301.04093
The success of machine learning solutions for reasoning about discrete structures has brought attention to its adoption within combinatorial optimization algorithms. Such approaches generally rely on supervised learning by leveraging datasets of the
Externí odkaz:
http://arxiv.org/abs/2203.08209
The design of additive imperceptible perturbations to the inputs of deep classifiers to maximize their misclassification rates is a central focus of adversarial machine learning. An alternative approach is to synthesize adversarial examples from scra
Externí odkaz:
http://arxiv.org/abs/2108.02756
Given a Markov decision process (MDP) and a linear-time ($\omega$-regular or LTL) specification, the controller synthesis problem aims to compute the optimal policy that satisfies the specification. More recently, problems that reason over the asympt
Externí odkaz:
http://arxiv.org/abs/2106.02951