Showing 3 open source projects for "joint"

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    iJEPA

    iJEPA

    Official codebase for I-JEPA

    i-JEPA (Image Joint-Embedding Predictive Architecture) is a self-supervised learning framework that predicts missing high-level representations rather than reconstructing pixels. A context encoder sees visible regions of an image and predicts target embeddings for masked regions produced by a slowly updated target encoder, focusing learning on semantics instead of texture.
    Downloads: 0 This Week
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    Awesome Graph Classification

    Awesome Graph Classification

    Graph embedding, classification and representation learning papers

    A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations. Relevant graph classification benchmark datasets are available. Similar collections about community detection, classification/regression tree, fraud detection, Monte Carlo tree search, and gradient boosting papers with implementations.
    Downloads: 1 This Week
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    SLM-Topo

    SLM-Topo

    Topology optimisation designed for laser based additive manufacturing

    ...In this homepage, the generation of a material database as well as the development of the topology optimization method is introduced and the impact on the component design is presented. SLM-Topo is a joint research project of the Institute of Product Engineering (IPEK) and the Institute for Applied Materials (IAM) of the Karlsruhe Institute for Technology (KIT) funded by the Deutsche Forschungsgemeinschaft (DFG).
    Downloads: 0 This Week
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