Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper
Query: Multimodal Deep Boltzmann Machines for feature selection on gene expression data
Data mining for feature selection in gene expression autism data
Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
Efficient Feature Selection via Analysis of Relevance and Redundancy
Analysis of Feature Selection Techniques: A Data Mining Approach
McTwo: a two-step feature selection algorithm based on maximal information coefficient
Semi-Supervised Generative Adversarial Network for Gene Expression Inference
Algorithms for feature selection: An evaluation
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data
Modeling Documents with a Deep Boltzmann Machine
Modeling Documents with Deep Boltzmann Machines
Practical Feature Subset Selection for Machine Learning
Simultaneous feature selection and clustering using mixture models
A comparative analysis of biclustering algorithms for gene expression data
UDSFS: Unsupervised deep sparse feature selection
A selective overview of feature screening for ultrahigh-dimensional data
Logic Classification and Feature Selection for Biomedical Data
Conditional Dynamic Mutual Information-Based Feature Selection
Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method
Margin based feature selection - theory and algorithms
Models of tone for tonal and non-tonal languages
Interpretable machine learning: definitions, methods, and applications
Wrappers for feature subset selection
Using denoising autoencoder for emotion recognition.
Data Encoding Visualization Based Cognitive Emotion Recognition with AC-GAN Applied for Denoising