Popular GitHub Repositories for Fold

Implementation of Alpha Fold 3 from the paper: "Accurate structure prediction of biomolecular interactions with AlphaFold3" in PyTorch

No-Frills Functional Programming Lib Augmenting Javascript/Node.js

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract)

The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 74 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS or on generative models using GANs to improve their quality.

Graph neural network for generating novel amino acid sequences that fold into proteins with predetermined topologies.

[arXiv] The official code for "Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived Classification".

Code and supplementary materials for the research paper titled 'Advancing Geological Image Segmentation: Deep Learning Approaches for Rock Type Identification and Classification', published in Applied Computing and Geosciences (Elsevier).

A set of tools for machine learning (for the current day, there are active learning utilities and implementations of some stacking-based techniques).

基于3D全身照片的皮肤癌检测

A comprehensive data science project for analysing eCormmerce and online shops data for possibility to enegage customer retention to increase purchases. Trained and comprehensively evaluated machine learning models using different algorithms and tuning procedures.

BUDGET : VotingRegressor(XGBoost+LightGBM) * (5 Fold CV) — This model, built for a Kaggle insurance regression competition, preprocesses data by imputing missing values (KNN), cleaning, and engineering new features. Statistical analysis reduces features before encoding and scaling for machine learning

From-scratch implementations of PCA, FDA, LDA, QDA for MNIST classification, decision trees, ensemble methods (bagging, random forest), 5-fold cross-validation, AdaBoost, Gradient Boosting, and neural networks for binary classification—using only NumPy, Pandas, and Matplotlib.

Proyecto de investigación en ML para identificar factores genéticos en pronóstico de lesiones pre-tumorales. Aprendizaje no supervisado para discernir perfiles genéticos distintivos entre grupos de buen y mal pronóstico, mejorando detección y tratamiento temprano del cáncer.

Effective deep learning approaches for animal classification.