3 papers
cs.LG2026
Learning Faster without Deeper Networks: A*-Inspired Batch Selection for Efficient CNN Training
Anxhelo Shehu, Enes Stastoli, Arben Cela
Common practice when training Convolutional Neural Networks (CNNs) is to use randomly shuffled mini-batches. This creates two limitations: slower convergence, and a diminishing lea…
cs.LG2025
Dynamic Perturbed Adaptive Method for Infinite Task-Conflicting Time Series
Jiang You, Xiaozhen Wang, Arben Cela
We formulate time series tasks as input-output mappings under varying objectives, where the same input may yield different outputs. This challenges a model's generalization and ada…
cs.LG2024
Learning K-U-Net with constant complexity: An Application to time series forecasting
Jiang You, Arben Cela, René Natowicz +2
Training deep models for time series forecasting is a critical task with an inherent challenge of time complexity. While current methods generally ensure linear time complexity, ou…