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cs.LG2026
Scale When Needed: Adaptive Neuron-level Mixed Precision Quantization Aware Training
Ayush K. Varshney, Konstantinos Vandikas, Šarūnas Girdzijauskas +2
Deploying deep neural networks on resource-constrained 6G edge devices demands aggressive compression with minimal accuracy loss. Quantization-Aware Training (QAT) has emerged as a…
cs.LG2026
When to restart? Exploring escalating restarts on convergence
Ayush K. Varshney, Šarūnas Girdzijauskas, Konstantinos Vandikas +1
Learning rate scheduling plays a critical role in the optimization of deep neural networks, directly influencing convergence speed, stability, and generalization. While existing sc…
cs.LG2025
Unlearning Clients, Features and Samples in Vertical Federated Learning
Ayush K. Varshney, Konstantinos Vandikas, Vicenç Torra
Federated Learning (FL) has emerged as a prominent distributed learning paradigm. Within the scope of privacy preservation, information privacy regulations such as GDPR entitle use…