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Tolga Dimlioglu

2 accepted papers

2026

Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems

CVPR 2026

Large-scale deep learning models for physical AI applications depend on diverse training data collection efforts. These models and correspondingly, the training data, must address the different evaluation criteria necessary for the models to be deployable in real-world environments. Data selection p

Cited by 0SourceScholar
2024

GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models

AISTATS 2024poster

We study distributed training of deep learning models in time-constrained environments. We propose a new algorithm that periodically pulls workers towards the center variable computed as a weighted average of workers, where the weights are inversely proportional to the gradient norms of the workers…