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The paper proposes an amortized framework for learning bandwidth selection in kernel density estimation under logarithmic score, showing consistent performance gains over classical methods in various sampling scenarios.
This paper presents a novel unsupervised data augmentation method combining Gaussian Mixture Models and Large Language Models to improve clustering on imbalanced text datasets by generating synthetic documents for underrepresented clusters.
This paper identifies a mismatch between training (winner-take-all loss) and inference in trajectory forecasting models, leading to uninformative mode probabilities. It proposes post-hoc treatments using posterior-weighted merging and a one-step EM update to improve mode ranking and forecasting accuracy without retraining.
Flash-GMM introduces a fused Triton kernel for Gaussian Mixture Models that achieves 20x speedup and enables training on datasets 100x larger on a single GPU, making soft clustering a viable drop-in replacement for k-means in approximate nearest neighbor search.
This paper introduces a probabilistic polygonal representation for plane curves using Gaussian Mixture Models, preserving local tangent, normal, and arc length while encoding uncertainty in the normal direction. The framework applies to various plane curves and supports uncertainty-aware geometric modeling for CAD, robotics, and trajectory planning.