3 projects for "method" with 2 filters applied:

  • Gemini 3 and 200+ AI Models on One Platform Icon
    Gemini 3 and 200+ AI Models on One Platform

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    Auth0 B2B Essentials: SSO, MFA, and RBAC Built In

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  • 1
    Exposure Correction

    Exposure Correction

    Learning multi-scale deep model correcting over- and under- exposed

    Exposure_Correction is a research project that provides the implementation for the paper Learning Multi-Scale Photo Exposure Correction (CVPR 2021). The repository focuses on correcting poorly exposed photographs, handling both underexposure and overexposure using a deep learning approach. The method employs a multi-scale framework that learns to enhance images by adjusting exposure levels across different spatial resolutions. This allows the model to preserve fine details while correcting global lighting inconsistencies. The repository includes pre-trained models, datasets, and training/testing code to enable reproducibility and experimentation. ...
    Downloads: 2 This Week
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  • 2
    Robust Tube MPC

    Robust Tube MPC

    Example implementation for robust model predictive control using tube

    ...Tube-based MPC achieves robustness by combining a nominal trajectory planner with an error feedback controller that keeps the actual system state within a "tube" around the nominal trajectory. This repository includes example scripts and implementations demonstrating how to apply the method to control problems. It is particularly useful for researchers, students, and engineers exploring robust control strategies in uncertain environments. By offering a structured implementation, robust-tube-mpc makes it easier to study and extend advanced MPC techniques for real-world applications.
    Downloads: 2 This Week
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  • 3
    deep-q-learning

    deep-q-learning

    Minimal Deep Q Learning (DQN & DDQN) implementations in Keras

    The deep-q-learning repository authored by keon provides a Python-based implementation of the Deep Q-Learning algorithm — a cornerstone method in reinforcement learning. It implements the core logic needed to train an agent using Q-learning with neural networks (i.e. approximating Q-values via deep nets), setting up environment interaction loops, experience replay, network updates, and policy behavior. For learners and researchers interested in reinforcement learning, this repo offers a concrete, runnable example bridging theory and practice: you can execute the code, play with hyperparameters, observe convergence behavior, and see how deep Q-learning learns policies over time in standard environments. ...
    Downloads: 0 This Week
    Last Update:
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