# Adapting Neural Models with Sequential Monte Carlo Dropout

arXiv: https://arxiv.org/abs/2210.15779 · HTML: https://mlsanity.com/paper/2210.15779/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2022.

## Built on by (most cited first, 2 of 2)

- [Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing](https://mlsanity.com/paper/2402.19298/) (2024)
- [DADM: Dual Alignment of Domain and Modality for Face Anti-spoofing](https://mlsanity.com/paper/2503.00429/) (2025)

## Abstract (arXiv)

The ability to adapt to changing environments and settings is essential for robots acting in dynamic and unstructured environments or working alongside humans with varied abilities or preferences. This work introduces an extremely simple and effective approach to adapting neural models in response to changing settings. We first train a standard network using dropout, which is analogous to learning an ensemble of predictive models or distribution over predictions. At run-time, we use a particle filter to maintain a distribution over dropout masks to adapt the neural model to changing settings in an online manner. Experimental results show improved performance in control problems requiring both online and look-ahead prediction, and showcase the interpretability of the inferred masks in a human behaviour modelling task for drone teleoperation.