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A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

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DINI: data imputation using neural inversion for edge applications.

Shikhar Tuli¹, Niraj K Jha²

¹Department of Electrical and Computer Engineering, Princeton, NJ, 08540, USA. stuli@princeton.edu.

Scientific Reports

|November 23, 2022

Summary

Data imputation using neural inversion (DINI) addresses missing data in edge AI systems. This model-agnostic framework significantly improves prediction accuracy in critical applications.

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Area of Science:

Computer Science
Artificial Intelligence
Edge Computing

Background:

Edge computing offers advantages in latency, bandwidth, energy efficiency, privacy, and security.
Deploying artificial intelligence (AI) models at the network edge (Edge-AI) is gaining traction for data-intensive applications.
Poor dataset quality, particularly missing data, is a significant challenge in edge computing environments.

Purpose of the Study:

To propose a novel data imputation strategy to mitigate the effects of corrupted data in Edge-AI systems.
To develop a model-agnostic framework applicable to various deep learning architectures for data imputation.

Main Methods:

Introduced Data Imputation using Neural Inversion (DINI), a framework that trains a surrogate model and performs data imputation.
DINI operates in an interleaved fashion, integrating imputation directly into the model training process.
The framework is designed to be model-agnostic, ensuring broad applicability across different deep learning architectures.

Main Results:

DINI demonstrated superior performance compared to state-of-the-art methods, achieving at least a 10.7% reduction in average imputation error.
Application of DINI in mission-critical scenarios led to prediction accuracy improvements up to 99% (F1 score of 0.99).
Significant performance gains were observed compared to baseline data imputation methods.

Conclusions:

DINI effectively addresses the challenge of missing data in Edge-AI systems.
The model-agnostic nature of DINI enhances its practical utility across diverse deep learning applications.
Implementing DINI can substantially increase the reliability and accuracy of AI models in mission-critical edge deployments.