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Found 7 papers, 1 papers with code
Identifying Corporate Credit Risk Sentiments from Financial News
To this end, we propose a novel deep learning-powered approach to automate news analysis and credit adverse events detection to score the credit sentiment associated with a company.
Computational Spectral Imaging with Unified Encoding Model: A Comparative Study and Beyond
Computational spectral imaging is drawing increasing attention owing to the snapshot advantage, and amplitude, phase, and wavelength encoding systems are three types of representative implementations.
Rethinking Boundary Discontinuity Problem for Oriented Object Detection
We decouple reversibility and joint-optim from single smoothing function into two distinct entities, which for the first time achieves the objectives of both correcting angular boundary and blending angle with other parameters. Extensive experiments on multiple datasets show that boundary discontinuity problem is well-addressed.
Contrastive Label Enhancement
Label distribution learning (LDL) is a new machine learning paradigm for solving label ambiguity.
Gaussian Label Distribution Learning for Spherical Image Object Detection
Therefore, we propose GLDL-ATSS as a better training sample selection strategy for objects of the spherical image, which can alleviate the drawback of IoU threshold-based strategy of scale-sample imbalance.
Bidirectional Loss Function for Label Enhancement and Distribution Learning
More specifically, this novel loss function not only considers the mapping errors generated from the projection of the input space into the output one but also accounts for the reconstruction errors generated from the projection of the output space back to the input one.
Generalized Label Enhancement with Sample Correlations
Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances.
