Efficient and Robust Machine Learning for Real-World Systems

by   Franz Pernkopf, et al.

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource efficient approaches. These approaches require a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. On top of this, it is crucial to treat uncertainty in a consistent manner in all but the simplest applications of machine learning systems. In particular, a desideratum for any real-world system is to be robust in the presence of outliers and corrupted data, as well as being `aware' of its limits, i.e. the system should maintain and provide an uncertainty estimate over its own predictions. These complex demands are among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology into every day's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. First we provide a comprehensive review of resource-efficiency in deep neural networks with focus on techniques for model size reduction, compression and reduced precision. These techniques can be applied during training or as post-processing and are widely used to reduce both computational complexity and memory footprint. As most (practical) neural networks are limited in their ways to treat uncertainty, we contrast them with probabilistic graphical models, which readily serve these desiderata by means of probabilistic inference. In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems.


page 1

page 12


Resource-Efficient Neural Networks for Embedded Systems

While machine learning is traditionally a resource intensive task, embed...

Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives

The amount of data processed in the cloud, the development of Internet-o...

A Survey on Distributed Machine Learning

The demand for artificial intelligence has grown significantly over the ...

Resource-Efficient Speech Mask Estimation for Multi-Channel Speech Enhancement

While machine learning techniques are traditionally resource intensive, ...

Generalized Uncertainty of Deep Neural Networks: Taxonomy and Applications

Deep neural networks have seen enormous success in various real-world ap...

State of the Art Review for Applying Computational Intelligence and Machine Learning Techniques to Portfolio Optimisation

Computational techniques have shown much promise in the field of Finance...

Probabilistic Deep Learning using Random Sum-Product Networks

Probabilistic deep learning currently receives an increased interest, as...

Please sign up or login with your details

Forgot password? Click here to reset