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    TTRL

    TTRL

    Test-Time Reinforcement Learning

    TTRL is an open-source framework for test-time reinforcement learning in large language models, with a particular focus on reasoning tasks where ground-truth labels are not available during inference. The project addresses the problem of how to generate useful reward signals from unlabeled test-time data, and its central insight is that common test-time scaling practices such as majority voting can be repurposed into reward estimates for online reinforcement learning. This makes the...
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    ChatGPT Shortcut

    ChatGPT Shortcut

    Curated AI prompt manager with search, sharing, and browser access

    ...In addition to built-in prompts, users can create, edit, and organize their own prompts for personal use. It also supports community participation where users can share prompts and vote on contributions, allowing useful prompts to surface through community feedback. Browser extension support further improves accessibility by enabling a sidebar interface.
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    VoteNet

    VoteNet

    Deep Hough Voting for 3D Object Detection in Point Clouds

    VoteNet is a 3D object detection framework for point clouds that combines deep point set networks with a Hough voting mechanism to localize and classify objects in 3D space. It tackles the challenge that object centroids in 3D scenes often don’t lie on any input surface point by having each point “vote” for potential object centers; these votes are then clustered to propose object hypotheses. Once cluster centers are formed, the network regresses bounding boxes around them and classifies them. VoteNet works end-to-end: it learns the voting, aggregation, and bounding-box regression components jointly, enabling strong detection accuracy without relying on 2D proxies or voxelization. ...
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  • 4
    libfastknn

    libfastknn

    Fast C++ KNN classifier

    KNN Classifier library for C++, at background using armadillo. In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.
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