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Logo for Code supporting the publication: BAN: detecting backdoors activated by adversarial neuron noise.

Code supporting the publication: BAN: detecting backdoors activated by adversarial neuron noise.

Code supporting the publication: BAN: detecting backdoors activated by adversarial neuron noise.

  • Adversarial Noise
  • Backdoor Defense
  • Backdoor Trigger Inversion
  • + 3
  • (JSON)
  • ("Jupyter Notebook")
  • (Markdown)
  • + 3
3
0
Logo for Deep Learning - Educational Material

Deep Learning - Educational Material

Deep Learning - Educational Material

  • deep learning
  • DL Assignments
  • Education
  • + 2
  • ("Jupyter Notebook")
  • (Markdown)
  • (Other)
1
0
Logo for diverse-projection-ensembles

diverse-projection-ensembles

Code accompanying the paper "Diverse projection ensembles for distributional reinforcement learning"

  • deep learning
  • Distributional Reinforcement Learning
  • Exploration
  • + 4
  • (Markdown)
  • (Other)
  • (Python)
3
0
Logo for FluvGAN: Python scripts to test generating and inverting fluvial deposits using GANs

FluvGAN: Python scripts to test generating and inverting fluvial deposits using GANs

FluvGAN: Python scripts to test generating and inverting fluvial deposits using GANs

  • deep generative modelling
  • deep learning
  • GAN
  • + 4
    2
    0
    Logo for laplace-vrnn

    laplace-vrnn

    Code underlying the publication: Bayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networks

    • Bayesian optimisation
    • codebase
    • deep learning
    • + 2
    • (Markdown)
    • (Other)
    • (Python)
    • + 2
    4
    0
    Logo for SMZ

    SMZ

    Code underlying the publication: VariBASed: Variational Bayes-Adaptive Sequential Monte-Carlo Planning for Deep Reinforcement Learning

    • Autonomous Path-Planning
    • Bayesian optimization
    • deep learning
    • + 3
    • ("Jupyter Notebook")
    • (large-text-file)
    • (Markdown)
    • + 4
    6
    0
    Logo for  Software underlying PhD thesis: AI in the Sky - Advancing Wildlife Survey Methods in Africa with Deep Learning and Aerial Imagery.

    Software underlying PhD thesis: AI in the Sky - Advancing Wildlife Survey Methods in Africa with Deep Learning and Aerial Imagery.

    Software underlying PhD thesis: AI in the Sky - Advancing Wildlife Survey Methods in Africa with Deep Learning and Aerial Imagery.

    • Artificial intelligence
    • deep learning
    • Monitoring
    • + 2
    • (Markdown)
    • (Python)
    1
    0
    Logo for Source code and data for the experiments presented in Deep Reinforcement Learning for Active Wake Control

    Source code and data for the experiments presented in Deep Reinforcement Learning for Active Wake Control

    Source code and data for the experiments presented in Deep Reinforcement Learning for Active Wake Control

    • active wake control
    • deep learning
    • Reinforcement learning
      4
      0
      Logo for Supplementary code to the paper: Flexible Enterprise Optimization With Constraint Programming

      Supplementary code to the paper: Flexible Enterprise Optimization With Constraint Programming

      Supplementary code to the paper: Flexible Enterprise Optimization With Constraint Programming

      • Constraint programming (Computer science)
      • deep learning
      • Enterprise engineering
      • + 2
        2
        0
        Logo for trtpi

        trtpi

        Code underlying the publication: Trust-Region Twisted Policy Improvement

        • Autonomous Path-Planning
        • deep learning
        • Markov decision process
        • + 2
        • ("Jupyter Notebook")
        • (Markdown)
        • (Other)
        • + 3
        4
        0

        ClimaNet

        A Climate Aware Spatio Temporal Encoder Decoder

        • Big data
        • climate
        • deep learning
        • ("Jupyter Notebook")
        • (Python)
        6
        0
        Logo for CoeusAI

        CoeusAI

        The CoeusAI QGIS plugin is designed for exploration of multiband geospatial datasets. It lets the user iteratively train and retrain segmentation models in seconds. A combination of Deep learning and traditional machine learning is used, leveraging the best of both methods.

        • deep learning
        • gis
        • Machine learning
        • + 2
        • (Python)
        3
        0