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Exploring the unknown
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Exploring the unknown

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AntreasAntoniou/README.md

Antreas Antoniou

I am a Machine Learning Research Associate at the University of Edinburgh, supervised by Prof. Amos Storkey. I am a member of the BayesWatch research group and the Adaptive and Neural Computation (ANC) research institute.

Leading my research interests is Multi-Modal Learning, specifically targeting the synergistic integration of text, images, audio, and video data. This is followed by the development of Self-Supervised Methods, inspired by mechanisms of infant learning and principles of evolutionary computation.

Additional key areas include Meta-Learning, Adversarial Learning, and Optimization Techniques Inspired by Evolutionary Optimization. These are applied across both differentiable and gradient-free optimization paradigms. Other relevant research dimensions include Inductive Biases, Scalability, Computational Efficiency, and Memory-Augmented Neural Networks.

For more information see my website: https://antreas.io/home/

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  1. HowToTrainYourMAMLPytorch HowToTrainYourMAMLPytorch Public

    The original code for the paper "How to train your MAML" along with a replication of the original "Model Agnostic Meta Learning" (MAML) paper in Pytorch.

    Python 774 137

  2. minimal-ml-template minimal-ml-template Public template

    A very minimal ml project template that uses HF transformers and wandb to train a simple NN and evaluate it, in a stateless manner compatible with Spot instances kubernetes workflows

    Python 35 6

  3. MatchingNetworks MatchingNetworks Public

    An attempt at replicating the Matching Networks for One Shot Learning in Tensorflow - Paper URL: https://arxiv.org/pdf/1606.04080.pdf

    Python 328 96

  4. DAGAN DAGAN Public

    DAGAN: Data Augmentation Generative Adversarial Networks

    Python 415 103

  5. kubejobs kubejobs Public

    Python 28 9

  6. CSTR-Edinburgh/mlpractical CSTR-Edinburgh/mlpractical Public template

    Machine Learning Practical course repository

    Jupyter Notebook 10 3