arXiv Machine Learning
Jun 17

Expanding SPHERE-JEPA: A Family of Statistical Regularizers for the Hypersphere

arXiv:2606. 17603v1 Announce Type: new Abstract: In Self-Supervised Learning (SSL), preventing representation collapse by explicitly enforcing a uniform distribution on the unit hypersphere has proven to be effective.

By L\'eo Nicollier (CB, ATT), Enric Meinhardt-Llopis (CB), Max Dunitz (ATT), Marc Pic (ATT), Pablo Mus\'e (CB, IFUMI), Gabriele Facciolo (CB)
arXiv AI
Sep 10

Revisiting Thinning Methods for Kernel Learning Problems

The paper introduces Backward Kernel Herding, an algorithm that iteratively removes data points to create representative subsets for kernel learning, achieving performance comparable to state‑of‑the‑art methods while speeding up subsampling when the reduced size is less than half the original dataset. It also proposes Flexible Kernel Thinning, an extension that allows construction of subsets of any size, not just successive halvings, and demonstrates that this method often yields the best predictive performance. Experiments on Gaussian Processes and Kernel Support Vector Machines show that Backward Kernel Herding excels in training‑time efficiency, while Flexible Kernel Thinning offers superior predictive accuracy and competitive memory usage, emphasizing the need to choose a reduction strategy based on the desired trade‑off between performance, cost, and memory.

By Blanca Cano-Camarero, Yago R. Aguado-Carrillo-de-Albornoz, \'Angela Fern\'andez-Pascual, Jos\'e R. Dorronsoro
arXiv AI
Jun 30

Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method

arXiv:2304. 11171v5 Announce Type: replace-cross Abstract: To overcome the limitations of point-based inputs, overly fine computation and limited adaptability in existing artificial intelligence methods, Guoyin Wang and Shuyin Xia proposed granular-ball computing as a new artificial intelligence learning paradigm.

By Shuyin Xia, Guoyin Wang, Xinbo Gao, Xiaoyu Lian, Hongzhi Kuai