no code implementations • 14 Dec 2023 • Huijie Zhang, Yifu Lu, Ismail Alkhouri, Saiprasad Ravishankar, Dogyoon Song, Qing Qu
This is due to the necessity of tracking extensive forward and reverse diffusion trajectories, and employing a large model with numerous parameters across multiple timesteps (i. e., noise levels).
1 code implementation • 12 Dec 2023 • Shijun Liang, Van Hoang Minh Nguyen, Jinghan Jia, Ismail Alkhouri, Sijia Liu, Saiprasad Ravishankar
To address this problem, we propose a novel image reconstruction framework, termed Smoothed Unrolling (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning approach.
1 code implementation • 11 Sep 2023 • Ismail Alkhouri, Shijun Liang, Rongrong Wang, Qing Qu, Saiprasad Ravishankar
In particular, we present a robustification strategy that improves the resilience of DL-based MRI reconstruction methods by utilizing pretrained diffusion models as noise purifiers.
no code implementations • 10 Jan 2023 • Ismail Alkhouri, Sumit Jha, Andre Beckus, George Atia, Alvaro Velasquez, Rickard Ewetz, Arvind Ramanathan, Susmit Jha
To measure the robustness of the predicted structures, we utilize (i) the root-mean-square deviation (RMSD) and (ii) the Global Distance Test (GDT) similarity measure between the predicted structure of the original sequence and the structure of its adversarially perturbed version.
no code implementations • 5 Jun 2021 • Alvaro Velasquez, Ismail Alkhouri, Andre Beckus, Ashutosh Trivedi, George Atia
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.
no code implementations • 3 Dec 2020 • George K. Atia, Andre Beckus, Ismail Alkhouri, Alvaro Velasquez
In this paper, we explore this steady-state planning problem that consists of deriving a decision-making policy for an agent such that constraints on its steady-state behavior are satisfied.
no code implementations • 10 May 2019 • Mohsen Joneidi, Ismail Alkhouri, Nazanin Rahnavard
A new paradigm for large-scale spectrum occupancy learning based on long short-term memory (LSTM) recurrent neural networks is proposed.