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A review on machine learning methods for in silico toxicity prediction
In silico toxicity prediction plays an important role in the regulatory decision making and
selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time …
selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time …
Content-aware local gan for photo-realistic super-resolution
Recently, GAN has successfully contributed to making single-image super-resolution (SISR)
methods produce more realistic images. However, natural images have complex distribution …
methods produce more realistic images. However, natural images have complex distribution …
Neighborhood linear discriminant analysis
Abstract Linear Discriminant Analysis (LDA) assumes that all samples from the same class
are independently and identically distributed (iid). LDA may fail in the cases where the …
are independently and identically distributed (iid). LDA may fail in the cases where the …
Robust sparse linear discriminant analysis
Linear discriminant analysis (LDA) is a very popular supervised feature extraction method
and has been extended to different variants. However, classical LDA has the following …
and has been extended to different variants. However, classical LDA has the following …
Tuberculosis disease diagnosis based on an optimized machine learning model
Computer science plays an important role in modern dynamic health systems. Given the
collaborative nature of the diagnostic process, computer technology provides important …
collaborative nature of the diagnostic process, computer technology provides important …
A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar
Identifying a reduced set of collective variables is critical for understanding atomistic
simulations and accelerating them through enhanced sampling techniques. Recently …
simulations and accelerating them through enhanced sampling techniques. Recently …
Data-driven collective variables for enhanced sampling
Designing an appropriate set of collective variables is crucial to the success of several
enhanced sampling methods. Here we focus on how to obtain such variables from …
enhanced sampling methods. Here we focus on how to obtain such variables from …
Linear discriminant analysis
Linear discriminant analysis (LDA) is a versatile statistical method for reducing redundant
and noisy information from an original sample to its essential features. Particularly, LDA is a …
and noisy information from an original sample to its essential features. Particularly, LDA is a …
Learning Robust Discriminant Subspace Based on Joint L₂,ₚ- and L₂,ₛ-Norm Distance Metrics
Recently, there are many works on discriminant analysis, which promote the robustness of
models against outliers by using L 1-or L 2, 1-norm as the distance metric. However, both of …
models against outliers by using L 1-or L 2, 1-norm as the distance metric. However, both of …
Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification
Person re-identification is to seek a correct match for a person of interest across different
camera views among a large number of impostors. It typically involves two procedures of …
camera views among a large number of impostors. It typically involves two procedures of …