arXiv:2606. 17858v1 Announce Type: new Abstract: Machine Learning (ML) techniques have been applied to various problems.
By Toshitaka Hayashi, Hamido Fujita, Dalibor Cimr, Richard Cimler, Jitka K\"uhnov\'a
arXiv:2607. 09100v1 Announce Type: cross Abstract: The rapid growth of image data has produced large-scale datasets, raising concerns about the time and memory costs of model training.
By Pedro Rocha Dantas, Lucas Pascotti Valem
arXiv:2606. 11761v1 Announce Type: new Abstract: Dynamic data pruning techniques aim to reduce computational cost while minimizing information loss by periodically selecting representative subsets of input data during model training.
By Atif Hassan, Swanand Khare, Jiaul H. Paik
arXiv:2606. 05814v1 Announce Type: new Abstract: The support vector machine (SVM) is a widely used classifier, but choosing an appropriate loss function remains difficult.
By Yuliang Yang, Chen Chen, Yuxiang Liu, Huiru Wang
arXiv:2606. 19712v1 Announce Type: new Abstract: While much effort has focused on developing and benchmarking high-performance neural networks, less attention has been given to how dataset properties, known to practitioners, can guide efficient model selection.
By Bryan Bo Cao, Abhinav Sharma, Lawrence O'Gorman, Michael Coss, Shubham Jain
arXiv:2606. 03292v1 Announce Type: cross Abstract: Many systems used in real-world environments require adding new categories and incorporating new information without forgetting what was previously learnt by the classification model.
By Pablo Garc\'ia-Santaclara, Bruno Fern\'andez-Castro, Rebeca Pilar D\'iaz-Redondo