A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.

Microarchitectural exploitation and other hardware attacks.

State-of-the-art neural cardinality estimators for join queries

TIP2022 Adaptive Boosting (AdaBoost) for Domain Adaptation ? 🤷‍♀️ Why not ! 🙆‍♀️

Reinforced Data Sampling

Implémentation d'un modèle de scoring (OpenClassrooms | Data Scientist | Projet 7)

This project aims to analyze the citation network of arXiv papers. We use Python to clean the data and create a Neo4j network to visualize and analyze the citation relationships between arXiv papers.

This file covers all concepts of R language..

Generating realistic test data or simulating load with authentic, dynamic data using the Gatling framework and JavaFaker

BiBiER: A Bilingual Bimodal Emotion Recognition Method with Improved Generalization through Data Augmentation

Dynamic cluster-based data sampling for efficient and long-tail-aware vision-language model pre-training.

Adaptive data sampling and transmission in a wireless sensor node as a function of energy reserves

Fraud detection in credit cards using Random Forest & strategic sampling | Detecção de fraudes em cartões de crédito com Random Forest e amostragem estratégica.

This repository contains experiments on data wrangling techniques, focusing on methods for handling missing values, filtering, aggregation, and more.

Process of data preparaton in R.

A Python package for flexible subset selection for data visualization.

Some nlp utils file I write to reuse

Professional Python tool for intelligently selecting and copying media files with advanced filtering, performance optimization, and resume capabilities. Perfect for dataset creation, content curation, and large-scale media management.

Here is Task 5: Credit card fraud detection using machine learning, for my data science internship with Codsoft

Code and Data for paper: Variation across Scales: Measurement Fidelity under Twitter Data Sampling (ICWSM '20)