# A Comparative Analysis on the Performance of Upper Confidence Bound Algorithms in Adaptive Deep Neural Networks

arXiv: https://arxiv.org/abs/2604.24810 · HTML: https://mlsanity.com/paper/2604.24810/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 27 Apr 2026.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
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- [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017)
- [DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference](https://mlsanity.com/paper/2004.12993/) (2020)
- [BERT Loses Patience: Fast and Robust Inference with Early Exit](https://mlsanity.com/paper/2006.04152/) (2020)
- [FastBERT: a Self-distilling BERT with Adaptive Inference Time](https://mlsanity.com/paper/2004.02178/) (2020)
- [Do CIFAR-10 Classifiers Generalize to CIFAR-10?](https://mlsanity.com/paper/1806.00451/) (2018)
- [Bandits with Knapsacks](https://mlsanity.com/paper/1305.2545/) (2013)
- [Resourceful Contextual Bandits](https://mlsanity.com/paper/1402.6779/) (2014)
- [LGViT: Dynamic Early Exiting for Accelerating Vision Transformer](https://mlsanity.com/paper/2308.00255/) (2023)
- [Fast yet Safe: Early-Exiting with Risk Control](https://mlsanity.com/paper/2405.20915/) (2024)
- [Fixing Overconfidence in Dynamic Neural Networks](https://mlsanity.com/paper/2302.06359/) (2023)
- [SplitEE: Early Exit in Deep Neural Networks with Split Computing](https://mlsanity.com/paper/2309.09195/) (2023)
- [CEEBERT: Cross-Domain Inference in Early Exit BERT](https://mlsanity.com/paper/2405.15039/) (2024)
- [Unsupervised Early Exit in DNNs with Multiple Exits](https://mlsanity.com/paper/2209.09480/) (2022)
- [Conditional computation in neural networks: principles and research trends](https://mlsanity.com/paper/2403.07965/) (2024)
- [Early-exit deep neural networks for distorted images: providing an efficient edge offloading](https://mlsanity.com/paper/2108.09343/) (2021)
- [Beyond Greedy Exits: Improved Early Exit Decisions for Risk Control and Reliability](https://mlsanity.com/paper/2509.23666/) (2025)

## Abstract (arXiv)

Edge computing environments impose strict constraints on energy consumption and latency, making the deployment of deep neural networks a significant challenge. Therefore, smart and adaptive inference strategies that dynamically balance computational cost or latency with predictive accuracy are critical in edge computing scenarios. In this work, we build on Adaptive Deep Neural Networks (ADNNs) that employ the Multi-Armed Bandit (MAB) framework. Current literature leverages the first version of the Upper Confidence Bound (UCB1) strategy to dynamically select the optimal confidence threshold, enabling efficient early exits without sacrificing accuracy. However, we introduce four additional Upper Confidence Bound strategies in ADNNs, namely UCB-V, UCB-Tuned, UCB-Bayes, and UCB-BwK, and perform, for the first time, a comparative study of these strategies with respect to trade-offs between accuracy, energy consumption, and latency. The proposed UCB strategies are employed on the ResNet and MobileViT neural networks, and are evaluated on the benchmark datasets of CIFAR-10, CIFAR-10.1, and CIFAR-100. Experimental results demonstrate that all strategies achieve sub-linear cumulative regret, with UCB-Bayes converging the fastest, followed by UCB-Tuned and UCB-V. Finally, UCB-V and UCB-Tuned dominate the Pareto Frontiers of accuracy-latency and accuracy-energy trade-offs. The implementation code is available here: https://github.com/gr3gor1/MAB_UCB