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Found 18 papers, 12 papers with code
skscope: Fast Sparsity-Constrained Optimization in Python
Applying iterative solvers on sparsity-constrained optimization (SCO) requires tedious mathematical deduction and careful programming/debugging that hinders these solvers' broad impact.
Robust Offline Reinforcement learning with Heavy-Tailed Rewards
This paper endeavors to augment the robustness of offline reinforcement learning (RL) in scenarios laden with heavy-tailed rewards, a prevalent circumstance in real-world applications.
A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models
SIMs provide an interpretable and flexible modeling framework for high-dimensional data, while best subset selection aims to find a sparse model from a large set of predictors.
Best-Subset Selection in Generalized Linear Models: A Fast and Consistent Algorithm via Splicing Technique
In high-dimensional generalized linear models, it is crucial to identify a sparse model that adequately accounts for response variation.
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning
On the other hand, the segmentation-based perceptual loss increases $+0. 14$ dB PSNR on average for SOTA methods, including CNNs and vision transformers.
An Instrumental Variable Approach to Confounded Off-Policy Evaluation
Off-policy evaluation (OPE) is a method for estimating the return of a target policy using some pre-collected observational data generated by a potentially different behavior policy.
Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process
In this paper, we show that with some auxiliary variables that mediate the effect of actions on the system dynamics, the target policy's value is identifiable in a confounded Markov decision process.
abess: A Fast Best Subset Selection Library in Python and R
In addition, a user-friendly R library is available at the Comprehensive R Archive Network.
MIASSR: An Approach for Medical Image Arbitrary Scale Super-Resolution
We also employ transfer learning to enable MIASSR to tackle SR tasks of new medical modalities, such as cardiac MR images (ACDC) and chest computed tomography images (COVID-CT).
Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives
However, one drawback of MRI is its comparatively slow scanning and reconstruction compared to other image modalities, limiting its usage in some clinical applications when imaging time is critical.
A Splicing Approach to Best Subset of Groups Selection
Best subset of groups selection (BSGS) is the process of selecting a small part of non-overlapping groups to achieve the best interpretability on the response variable.
Computational analysis of pathological image enables interpretable prediction for microsatellite instability
Microsatellite instability (MSI) is associated with several tumor types and its status has become increasingly vital in guiding patient treatment decisions.
A Fast and Effective Method of Macula Automatic Detection for Retina Images
Retina image processing is one of the crucial and popular topics of medical image processing.
Arbitrary Scale Super-Resolution for Brain MRI Images
A recent paper has proposed a novel meta-learning technique that uses a Weight Prediction Network to enable Super-Resolution on arbitrary scale factors using only a single neural network.
How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach
Single image super-resolution (SISR) is of great importance as a low-level computer vision task.
Ball: An R package for detecting distribution difference and association in metric spaces
The rapid development of modern technology facilitates the appearance of numerous unprecedented complex data which do not satisfy the axioms of Euclidean geometry, while most of the statistical hypothesis tests are available in Euclidean or Hilbert spaces.
Computation
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Lesion Focused Super-Resolution
Super-resolution (SR) for image enhancement has great importance in medical image applications.
