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Found 10 papers, 1 papers with code
Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention
Participants preferred the adaptive interventions and rated the system highly on intervention time accuracy, effectiveness, and level of trust.
Multi-perspective Feedback-attention Coupling Model for Continuous-time Dynamic Graphs
Recently, representation learning over graph networks has gained popularity, with various models showing promising results.
Joint Channel Estimation and Turbo Equalization of Single-Carrier Systems over Time-Varying Channels
Block transmission systems have been proven successful over frequency-selective channels.
Towards Accurate and Compact Architectures via Neural Architecture Transformer
To address this issue, we propose a Neural Architecture Transformer++ (NAT++) method which further enlarges the set of candidate transitions to improve the performance of architecture optimization.
An Improved Iterative Neural Network for High-Quality Image-Domain Material Decomposition in Dual-Energy CT
We propose a new INN architecture for DECT material decomposition.
Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
The proposed learning formulation combines both unsupervised learning-based priors (or even simple analytical priors) together with (supervised) deep network-based priors in a unified MBIR framework based on a fixed point iteration analysis.
SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction
Recent works have shown the promising reconstruction performance of methods such as PWLS-ULTRA that rely on clustering the underlying (reconstructed) image patches into a learned union of transforms.
Combinatorial Keyword Recommendations for Sponsored Search with Deep Reinforcement Learning
In sponsored search, keyword recommendations help advertisers to achieve much better performance within limited budget.
DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering
Dual energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability.
Sparse-View X-Ray CT Reconstruction Using $\ell_1$ Prior with Learned Transform
Our results with the extended cardiac-torso (XCAT) phantom data and clinical chest data show that, for sparse-view 2D fan-beam CT and 3D axial cone-beam CT, PWLS-ST-$\ell_1$ improves the quality of reconstructed images compared to the CT reconstruction methods using edge-preserving regularizer and $\ell_2$ prior with learned ST.
