Piotr Kubaty — accepted AI-conference papers

2026

Rethinking Calibration for Early-Exit Neural Networks

Piotr Kubaty, Filip Szatkowski, Grzegorz Choczyński, Bartosz Wójcik, Eric Nalisnick

ICML 2026poster

Early-exit neural networks~(EENNs) accelerate inference by allowing intermediate classifiers to stop computation once predictions are confident enough. Most methods rely on confidence thresholds for exiting, and consequently, classifier calibration is widely assumed to improve performance. In this w…

Cited by 0 Source ↗Scholar ↗

2025

How to Train Your Multi-Exit Model? Analyzing the Impact of Training Strategies

Piotr Kubaty, Bartosz Wójcik, Bartłomiej Tomasz Krzepkowski, Monika Michaluk, Tomasz Trzcinski, Jary Pomponi +1

ICML 2025poster

Early exits enable the network's forward pass to terminate early by attaching trainable internal classifiers to the backbone network. Existing early-exit methods typically adopt either a joint training approach, where the backbone and exit heads are trained simultaneously, or a disjoint approach, wh…

Cited by 0 Source ↗PDF ↗Scholar ↗