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Found 5 papers, 0 papers with code
Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI
HP-ALF can perceive the image information in the hierarchical mechanism: image-level perception and patch-level perception.
Explainable Recommendation Systems by Generalized Additive Models with Manifest and Latent Interactions
In this paper, we propose the explainable recommendation systems based on a generalized additive model with manifest and latent interactions (GAMMLI).
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness
The performance of traditional compressive sensing-based MRI (CS-MRI) reconstruction is affected by its slow iterative procedure and noise-induced artefacts.
A Machine Learning Framework for Stock Selection
The effectiveness of the stock selection strategy is validated in Chinese stock market in both statistical and practical aspects, showing that: 1) Stacking outperforms other models reaching an AUC score of 0. 972; 2) Genetic Algorithm picks a subset of 114 features and the prediction performances of all models remain almost unchanged after the selection procedure, which suggests some features are indeed redundant; 3) LR and DNN are radical models; RF is risk-neutral model; Stacking is somewhere between DNN and RF.
Robust Log-Optimal Strategy with Reinforcement Learning
We proposed a new Portfolio Management method termed as Robust Log-Optimal Strategy (RLOS), which ameliorates the General Log-Optimal Strategy (GLOS) by approximating the traditional objective function with quadratic Taylor expansion.
