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Found 12 papers, 2 papers with code
U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting
Time series forecasting is a crucial task in various domains.
D3Former: Jointly Learning Repeatable Dense Detectors and Feature-enhanced Descriptors via Saliency-guided Transformer
Notably, tests on 3DLoMatch, even with a low overlap ratio, show that our method consistently outperforms recently published approaches such as RoReg and RoITr.
Efficient and Effective Deep Multi-view Subspace Clustering
i) The parameter scale of the FC layer is quadratic to sample numbers, resulting in high time and memory costs that significantly degrade their feasibility in large-scale datasets.
MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting
Time series forecasting has received wide interest from existing research due to its broad applications and inherent challenging.
Tensor Robust PCA with Nonconvex and Nonlocal Regularization
To this end, we integrate nonlocal self-similarity into N-TRPCA, and further develop a nonconvex and nonlocal TRPCA (NN-TRPCA) model.
A similarity measurement for time series and its application to the stock market
In this paper, for accurately describing the similarity between a pair of time series, a novel similarity measurement is proposed, which is named as the dynamic multi-perspective personalized similarity measurement (DMPSM).
A Collaborative Attention Adaptive Network for Financial Market Forecasting
However, taking into account the differences of different data types, how to use a fusion method adapted to financial data to fuse real market prices and tweets from social media, so that the prediction model can fully integrate different types of data remains a challenging problem.
Context-aware virtual adversarial training for anatomically-plausible segmentation
Despite their outstanding accuracy, semi-supervised segmentation methods based on deep neural networks can still yield predictions that are considered anatomically impossible by clinicians, for instance, containing holes or disconnected regions.
Self-paced and self-consistent co-training for semi-supervised image segmentation
Moreover, to encourage predictions from different networks to be both consistent and confident, we enhance this generalized JSD loss with an uncertainty regularizer based on entropy.
Developing Univariate Neurodegeneration Biomarkers with Low-Rank and Sparse Subspace Decomposition
With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25$\%$ reduction in the mean annual change with 80$\%$ power and two-tailed $P=0. 05$ are 116, 279 and 387 for the longitudinal $A\beta+$ AD, $A\beta+$ mild cognitive impairment (MCI) and $A\beta+$ CU groups, respectively.
Texture Relative Superpixel Generation With Adaptive Parameters
Abstract—Superpixel generation, which is an essential step in many image processing applications, has attracted increasing attention from researchers.
Optical Fringe Patterns Filtering Based on Multi-Stage Convolution Neural Network
Optical fringe patterns are often contaminated by speckle noise, making it difficult to accurately and robustly extract their phase fields.
