Search Results for author:
Found 13 papers, 1 papers with code
Domain-conditioned and Temporal-guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction
Purpose: To propose a domain-conditioned and temporal-guided diffusion modeling method, termed dynamic Diffusion Modeling (dDiMo), for accelerated dynamic MRI reconstruction, enabling diffusion process to characterize spatiotemporal information for time-resolved multi-coil Cartesian and non-Cartesian data.
GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant Attention
In this manuscript, we seek an interpretable construction of a convolutional network with a nonlocal self-similarity prior that performs on par with black-box nonlocal models.
IIP-Mixer:Intra-Inter Patch Mixing Architecture for Battery Remaining Useful Life Prediction
Notably, to address the varying importance of features in RUL prediction, we introduce a weighted loss function in the MLP-Mixer-based architecture, marking the first time such an approach has been employed.
Quantum Mixed-State Self-Attention Network
This paper introduces a novel Quantum Mixed-State Self-Attention Network (QMSAN) for natural language processing tasks.
DeepEMC-T2 Mapping: Deep Learning-Enabled T2 Mapping Based on Echo Modulation Curve Modeling
The modified U-Net employs several new features to improve the accuracy of T2/PD estimation.
DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification
This article proposes a dual-network-based feature extractor, perceptive capsule network (PCapN), for multivariate time series classification (MTSC), including a local feature network (LFN) and a global relation network (GRN).
1D-Touch: NLP-Assisted Coarse Text Selection via a Semi-Direct Gesture
We introduce 1D-Touch, a novel text selection method that complements the carets-based sub-word selection by facilitating the selection of semantic units of words and above.
Joint Spectrum and Power Allocation for V2X Communications with Imperfect CSI
More specifically, Bernstein approximations are employed to convert the chance constraint into a calculable constraint, and Bisection search method is proposed to obtain the optimal allocation solution with low complexity.
Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networks
While most of these methods focus on designing novel reconstruction networks or new training strategies for a given undersampling pattern, e. g., Cartesian undersampling or Non-Cartesian sampling, to date, there is limited research aiming to learn and optimize k-space sampling strategies using deep neural networks.
Learning Oculomotor Behaviors from Scanpath
Identifying oculomotor behaviors relevant for eye-tracking applications is a critical but often challenging task.
Interaction Detection Between Vehicles and Vulnerable Road Users: A Deep Generative Approach with Attention
Intersections where vehicles are permitted to turn and interact with vulnerable road users (VRUs) like pedestrians and cyclists are among some of the most challenging locations for automated and accurate recognition of road users' behavior.
Few-Shot Meta-Learning on Point Cloud for Semantic Segmentation
With a good understanding of environmental information, construction robots can work better.
SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction
The undersampled images are generated by a fixed undersampling pattern in the training, and the trained network is then applied to reconstruct new images acquired with the same pattern in the inference.
