Pytorch implementation of the paper 'Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding' (AAAI2024).
Learning second order dynamical system
Python library for adaptive Gaussian mixture state estimation. Useful for navigation and tracking in nonlinear non-Gaussian systems. Capable of incorporating negative information and other imprecise evidence.
Unsupervised learning algorithms to cluster students of a public school
Implemented an auto-clustering tool with seed and number of clusters finder. Optimizing algorithms: Silhouette, Elbow. Clustering algorithms: k-Means, Bisecting k-Means, Gaussian Mixture. Module includes micro-macro pivoting, and dashboards displaying radius, centroids, and inertia of clusters. Used: Python, Pyspark, Matplotlib, Spark MLlib.
Machine learning (BGMM/GP) code for the Catassembly Triad framework in JACS. Validates catalyst efficacy prediction via triad descriptors (attachability, controllability, detachability).
A minimal working example of the spectral mixture kernel
A novel image compressor based on a mixed integer linear program
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