LibMOON is a standard and flexible framework to study gradient-based multiobjective optimization.
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Updated
Mar 28, 2025 - Python
LibMOON is a standard and flexible framework to study gradient-based multiobjective optimization.
Gait Recognition with 3D CNN. This project proposes a novel approach using 3D convolutional neural networks (3D CNN) to capture spatio-temporal features of gait sequences for robust recognition in an un-intrusive manner.
Selected Paper from the AI-CyberSec 2021 Workshop in the 41st SGAI International Conference on Artificial Intelligence (MDPI Journal Electronics)
Time-series forecasting in Rust based on prophet
pipeDejavu: Hardware-aware Latency Predictable, Differentiable Search for Faster Config and Convergence of Distributed ML Pipeline Parallelism
We're proud to announce that our team secured the First Prize in the BCG GAMMA Challenge for Data Science and Consulting. This GitHub repository hosts all the code, models, and analytical techniques we employed during the competition.
Aplicação Python+Streamlit para detectar automaticamente colônias bacterianas em imagens de placas de Petri. Usa visão computacional com transformada de Hough, otimização bayesiana para calibrar parâmetros e permite validação manual dos resultados com exportação de imagens. Ideal para uso laboratorial e educacional.
Molecular active learning with JAX
A comparative study of Custom CNNs vs. Finetuning for garbage classification. Includes rigorous explainability analysis (Feature Maps), hyperparameter sweeping, and quantization benchmarks (FP16/INT8).
A Python package for machine learning pipeline optimization
MBTI mental health analysis with ML and Bayesian tuning
Human-in-the-Loop Bayesian optimization system that learns from ordinal human feedback (A/B/C ranks) to propose optimal experiment conditions with minimal trials.
Hyperparameter tuning for CNN models on Fashion MNIST using KerasTuner. Includes random search and Bayesian optimization strategies to improve performance and training efficiency.
This project implements a high-performance pipeline for aerodynamic shape optimization. It uses Bayesian Optimization to discover ideal NACA 4-digit airfoils across a flight envelope and trains a Random Forest Surrogate Model to provide instantaneous aerodynamic predictions.
GUI for hyperparameter optimizer and plotting experiment results
This repository follows the work of Bidirectional Information Flow
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