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NoPA introduces a non-parametric distribution-based approach for real-time 3D scene graph generation, preserving geometric details using kernel density estimates and particle-based object representation, substantially outperforming current methods.
This paper introduces a non-parametric multi-view Gaussian process framework for detecting machine-generated text that is robust to adversarial manipulations like paraphrasing. By combining complementary features and providing calibrated uncertainty, it outperforms existing detectors on held-out attacks.
This paper introduces a distributional generalization of matrix completion where each entry is a probability distribution rather than a scalar, using kernel mean embeddings and Tucker rank to capture low-rank structure. The authors propose a novel estimator with non-asymptotic error bounds and demonstrate effectiveness on synthetic and real-world data.
Contrastive Reflection (CORE) is a non-parametric algorithm that generates concise, interpretable insights from comparing successful and unsuccessful reasoning traces, enabling faster and more efficient self-improvement for language models with fewer samples and rollouts than existing methods.
The paper proposes non-parametric estimators KM-ARL and KM-ADD for evaluating changepoint detectors under finite and irregular sequence lengths, drawing an analogy between QCD and survival analysis.
Flow-Direct introduces a non-parametric guidance field for flow-based generative models that accumulates reward feedback persistently, improving feedback efficiency and enabling reuse of collected samples to guide generation for multiple objectives without additional reward evaluations.