294 citations · 928 across the 63 of their papers we have counts for
71 papers
How Architecture and Training Affect TPC Representations Across Experiments
Tyler Wheeler, Michelle P. Kuchera, Raghuram Ramanujan +12
Deep-learning efforts have increasingly shifted toward foundation model approaches. In experimental physics, this allows models and learned representations to be reused beyond the…
ForcingDAS: Unified and Robust Data Assimilation via Diffusion Forcing
Yixuan Jia, Siyi Chen, Yida Pan +9
Data assimilation (DA) estimates the state of an evolving dynamical system from noisy, partial observations, and is widely used in scientific simulation as well as weather and clim…
Trajectory Constraints for Imaging Inverse Problems
Chaoyan Huang, Haijie Yuan, Saiprasad Ravishankar +1
Diffusion models have achieved strong performance in imaging inverse problems, but typically rely on domain-matched pretrained models and external training data. Moreover, errors i…
Fractional-gradient Sparsity with Autoencoding Sequential Deep Image Prior for 3D CT Reconstruction
Haijie Yuan, Chaoyan Huang, Srijita Bandopadhyay +2
3D volumetric reconstruction from incomplete or noisy measurements is a fundamental problem in medical imaging and computational tomography. Deep image prior (DIP)-based methods ha…
L-FAME: Longitudinal Focused Attention Meditation EEG Dataset and Benchmark
Angqi Li, Ab Basit Rafi Syed, Hamzeh Alzweri +3
We introduce a novel Longitudinal Focused Attention Meditation Electroencephalography (L-FAME) dataset and an accompanying benchmark, designed to foster research into the neural ef…
Dynamic MRI Reconstruction Via Dual Deep Priors and Low-Rank Plus Sparse Modeling
Yongliang Sun, Siddhant Gautam, Chaoyan Huang +3
Dynamic MRI reconstruction from undersampled measurements is a challenging inverse problem that requires preserving both spatial reconstruction quality and temporal consistency acr…
Scan-Adaptive Dynamic MRI Undersampling Using a Dictionary of Efficiently Learned Patterns
Siddhant Gautam, Angqi Li, Prachi P. Agarwal +4
Cardiac MRI is limited by long acquisition times, which can lead to patient discomfort and motion artifacts. We aim to accelerate Cartesian dynamic cardiac MRI by learning efficien…
Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction
Evan Bell, Shijun Liang, Ismail Alkhouri +1
Deep Image Prior (DIP) has recently emerged as a promising one-shot neural-network based image reconstruction method. However, DIP has seen limited application to 3D image reconstr…
NERD: Network-Regularized Diffusion Sampling For 3D Computed Tomography
Shijun Liang, Ismail Alkhouri, Qing Qu +2
Numerous diffusion model (DM)-based methods have been proposed for solving inverse imaging problems. Among these, a recent line of work has demonstrated strong performance by formu…
Sparse Methods for Vector Embeddings of TPC Data
Tyler Wheeler, Michelle P. Kuchera, Raghuram Ramanujan +7
Time Projection Chambers (TPCs) are versatile detectors that reconstruct charged-particle tracks in an ionizing medium, enabling sensitive measurements across a wide range of nucle…
KELP: K-space-conditioned Estimation of Learned Sampling Patterns for Scan-Adaptive Multi-Coil MRI
Aryan Dhar, Siddhant Gautam, Saiprasad Ravishankar
Deep learning techniques have gained considerable attention for accelerating MRI acquisition while maintaining image quality. In this work, we present a convolutional neural networ…
A Dataless Reinforcement Learning Approach to Rounding Hyperplane Optimization for Max-Cut
Gabriel Maliakal, Ismail Alkhouri, Alvaro Velasquez +2
The Maximum Cut (MaxCut) problem is NP-Complete, and obtaining its optimal solution is NP-hard in the worst case. As a result, heuristic-based algorithms are commonly used, though…
Variational Learning Finds Flatter Solutions at the Edge of Stability
Avrajit Ghosh, Bai Cong, Rio Yokota +5
Variational Learning (VL) has recently gained popularity for training deep neural networks. Part of its empirical success can be explained by theories such as PAC-Bayes bounds, min…
UGoDIT: Unsupervised Group Deep Image Prior Via Transferable Weights
Shijun Liang, Ismail R. Alkhouri, Siddhant Gautam +2
Recent advances in data-centric deep generative models have led to significant progress in solving inverse imaging problems. However, these models (e.g., diffusion models (DMs)) ty…
Learning Dynamics of Deep Linear Networks Beyond the Edge of Stability
Avrajit Ghosh, Soo Min Kwon, Rongrong Wang +2
Deep neural networks trained using gradient descent with a fixed learning rate often operate in the regime of "edge of stability" (EOS), where the largest eigenvalue of the Hes…
Understanding Untrained Deep Models for Inverse Problems: Algorithms and Theory
Ismail Alkhouri, Evan Bell, Avrajit Ghosh +3
In recent years, deep learning methods have been extensively developed for inverse imaging problems (IIPs), encompassing supervised, self-supervised, and generative approaches. Mos…
Learnable Scaled Gradient Descent for Guaranteed Robust Tensor PCA
Lanlan Feng, Ce Zhu, Yipeng Liu +2
Robust tensor principal component analysis (RTPCA) aims to separate the low-rank and sparse components from multi-dimensional data, making it an essential technique in the signal p…
Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)
Siddhant Gautam, Angqi Li, Nicole Seiberlich +2
Accelerated MRI involves collecting partial -space measurements to reduce acquisition time, patient discomfort, and motion artifacts, and typically uses regular undersampling pa…
Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization
Shijun Liang, Evan Bell, Avrajit Ghosh +1
Deep learning methods are highly effective for many image reconstruction tasks. However, the performance of supervised learned models can degrade when applied to distinct experimen…
Sequential Diffusion-Guided Deep Image Prior For Medical Image Reconstruction
Shijun Liang, Ismail Alkhouri, Qing Qu +2
Deep learning (DL) methods have been extensively applied to various image recovery problems, including magnetic resonance imaging (MRI) and computed tomography (CT) reconstruction.…
SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problems
Ismail Alkhouri, Shijun Liang, Cheng-Han Huang +4
Diffusion models (DMs) are a class of generative models that allow sampling from a distribution learned over a training set. When applied to solving inverse problems, the reverse s…
Learning Robust Features for Scatter Removal and Reconstruction in Dynamic ICF X-Ray Tomography
Siddhant Gautam, Marc L. Klasky, Balasubramanya T. Nadiga +3
Density reconstruction from X-ray projections is an important problem in radiography with key applications in scientific and industrial X-ray computed tomography (CT). Often, such…
Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at Initialization
Avrajit Ghosh, Xitong Zhang, Kenneth K. Sun +3
We introduce Optimal Eye Surgeon (OES), a framework for pruning and training deep image generator networks. Typically, untrained deep convolutional networks, which include image sa…
Decoupled Data Consistency with Diffusion Purification for Image Restoration
Xiang Li, Soo Min Kwon, Shijun Liang +3
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…