Tag: Data-centric AI
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Extended abstract of our IEEE DSAA24 paper is now available
The extended abstract of our paper, “p-ClustVal: A Novel p-adic Approach for Enhanced Clustering of High-Dimensional scRNASeq Data” is available at IEEE Xplore (https://ieeexplore.ieee.org/document/10722799)
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Enhancing Cluster Discernment in High Dimensional Single Cell RNA Sequencing Data
Excited to share that our recent work on using alternate data representation for enhancing cluster discernment in high dimensional single cell RNA Sequencing data has been accepted at the 11th IEEE International Conference on Data Science & Analytics (DSAA 2024).
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Accepted Paper: Enhancing K-Means with Data-Centric AI
Our paper – “What Data-Centric AI can Do for Kmeans: a Faster, Robust Kmeans-d” has been accepted at the Proceedings of the 41st International Conference on Machine Learning (ICML), Data-Centric Machine Learning Workshop (DMLR), to be held in Vienna, Austria.
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What Data-Centric AI Can Do For kmeans–a Faster, Robust kmeans-d
Parichit Sharma, Hasan Kurban, Mehmet M. Dalkilic – Proceedings of the 41st International Conference on Machine Learning (ICML), Vienna, Austria, Data Centric Machine Learning Research Workshop (DMLR), 2024 (Accepted)
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Geometric-k-means–An Unbounded, Accurate and Energy-Efficient k-means
Parichit Sharma, Hasan Kurban, Marcin Malec, Oguzhan Kulekci, Mehmet Dalkilic – Machine Learning (under-review)
Kurban Intelligence Lab 