3D Neural Denoising for Track Reconstruction and Pattern ID @ LHCb TORCH Detector
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Updated
Jun 5, 2024 - Python
3D Neural Denoising for Track Reconstruction and Pattern ID @ LHCb TORCH Detector
Official repository for "Blind Source Separation of Single-Channel Mixtures via Multi-Encoder Autoencoders".
This project explores techniques to develop efficient and scalable image classification tools for medical screening. Using deep learning models like CNNs and Autoencoders, it leverages low-resource datasets to advance healthcare diagnostics.
Collection of operational time series ML models and tools
Using k-means clustering approaches to reduce intraclass variability. We have assessed a traditional clustering pipeline (feature extraction + dimensionality reduction with AE's + K-Means).
Integrate your chemometric tools with the scikit-learn API 🧪 🤖
This project is a comparative study of Autoencoder (AE) and Principal Component Analysis (PCA) for dimensionality reduction in gene expression data. It aims to understand the unique capabilities and applications of both methods in handling high-dimensional biological data.
Nvidia DLI workshop on AI-based anomaly detection techniques using GPU-accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) and then implement and compare supervised and unsupervised learning techniques.
Python autoencoder to remove blur from images
Compressing images using Autoencoders and transferring them over the network
Tackle accent classification and conversion using audio data, leveraging MFCCs and spectrograms. Models differentiate accents and convert audio between accents
AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.
A Recommender System that predicts ratings from 1 to 5 on MovieLens 1M Dataset
There are C language computer programs about the simulator, transformation, and test statistic of continuous Bernoulli distribution. More than that, the book contains continuous Binomial distribution and continuous Trinomial distribution.
The project consists of implementing an autoencoder-based fraud detector
Detection of IoT devices infected by malwares from their network communications, using federated machine learning
Breast Cancer H&E classification of Images and Image Generation
Complexity Assessment of LC methods on CPU and GPU
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