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Found 8 papers, 1 papers with code
UR2M: Uncertainty and Resource-Aware Event Detection on Microcontrollers
This limitation hinders the feasibility of many important on-device wearable event detection (WED) applications, such as heart attack detection.
Balancing Continual Learning and Fine-tuning for Human Activity Recognition
These schemes re-purpose contrastive learning for knowledge retention and, Kaizen combines that with self-training in a unified scheme that can leverage unlabelled and labelled data for continual learning.
Kaizen: Practical Self-supervised Continual Learning with Continual Fine-tuning
Kaizen is able to balance the trade-off between knowledge retention and learning from new data with an end-to-end model, paving the way for practical deployment of continual learning systems.
Enhancing the Security & Privacy of Wearable Brain-Computer Interfaces
Consequently, they are susceptible to a multiplicity of attacks across the hardware, software, and networking stacks used that can leak users' brainwave data or at worst relinquish control of BCI-assisted devices to remote attackers.
High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm
Moreover, these pipelines are deterministic in nature, making them unable to capture predictive uncertainty.
Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs
Quantized neural networks (NN) are the common standard to efficiently deploy deep learning models on tiny hardware platforms.
Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data
To handle these issues, we propose an ensemble framework where multiple deep learning models for sound-based COVID-19 detection are developed from different but balanced subsets from original data.
The Benefit of the Doubt: Uncertainty Aware Sensing for Edge Computing Platforms
To meet the energy and latency requirements of these embedded platforms the framework is built from the ground up to provide predictive uncertainty based only on one forward pass and a negligible amount of additional matrix multiplications with theoretically proven correctness.
