Collaborating Authors

Non-Intrusive Load Monitoring with Fully Convolutional Networks Machine Learning

Non-intrusive load monitoring or energy disaggregation involves estimating the power consumption of individual appliances from measurements of the total power consumption of a home. Deep neural networks have been shown to be effective for energy disaggregation. In this work, we present a deep neural network architecture which achieves state of the art disaggregation performance with substantially improved computational efficiency, reducing model training time by a factor of 32 and prediction time by a factor of 43. This improvement in efficiency could be especially useful for applications where disaggregation must be performed in home on lower power devices, or for research experiments which involve training a large number of models.

WaveletFCNN: A Deep Time Series Classification Model for Wind Turbine Blade Icing Detection Machine Learning

Wind power, as an alternative to burning fossil fuels, is plentiful and renewable. Data-driven approaches are increasingly popular for inspecting the wind turbine failures. In this paper, we propose a novel classification-based anomaly detection system for icing detection of the wind turbine blades. We effectively combine the deep neural networks and wavelet transformation to identify such failures sequentially across the time. In the training phase, we present a wavelet based fully convolutional neural network (FCNN), namely WaveletFCNN, for the time series classification. We improve the original (FCNN) by augmenting features with the wavelet coefficients. WaveletFCNN outperforms the state-of-the-art FCNN for the univariate time series classification on the UCR time series archive benchmarks. In the detecting phase, we combine the sliding window and majority vote algorithms to provide the timely monitoring of the anomalies. The system has been successfully implemented on a real-world dataset from Goldwind Inc, where the classifier is trained on a multivariate time series dataset and the monitoring algorithm is implemented to capture the abnormal condition on signals from a wind farm.

MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning Artificial Intelligence

Recent studies on neural architecture search have shown that automatically designed neural networks perform as good as human-designed architectures. While most existing works on neural architecture search aim at finding architectures that optimize for prediction accuracy. These methods may generate complex architectures consuming excessively high energy consumption, which is not suitable for computing environment with limited power budgets. We propose MONAS, a Multi-Objective Neural Architecture Search with novel reward functions that consider both prediction accuracy and power consumption when exploring neural architectures. MONAS effectively explores the design space and searches for architectures satisfying the given requirements. The experimental results demonstrate that the architectures found by MONAS achieve accuracy comparable to or better than the state-of-the-art models, while having better energy efficiency.

New MIT Neural Network Architecture May Reduce Carbon Footprint by AI


Artificial Intelligence may seem transient, yet it always managed to have a controversial presence. Recently it raised concerns about its sustainability. In June 2019, the University of Massachusetts at Amherst study discovered that a single large (213 million parameters) Transformer-based neural network built using NAS (commonly used in machine translation) has produced around 626,000 pounds of carbon dioxide. This amount is equivalent to five times more than an average car produces in its lifespan. These massive consumption numbers are because of the energy needed to run specialized hardware like GPUs and TPUs for AI training and development.

End-to-End Learning for Structured Prediction Energy Networks Machine Learning

Structured Prediction Energy Networks (SPENs) are a simple, yet expressive family of structured prediction models (Belanger and McCallum, 2016). An energy function over candidate structured outputs is given by a deep network, and predictions are formed by gradient-based optimization. This paper presents end-to-end learning for SPENs, where the energy function is discriminatively trained by back-propagating through gradient-based prediction. In our experience, the approach is substantially more accurate than the structured SVM method of Belanger and McCallum (2016), as it allows us to use more sophisticated non-convex energies. We provide a collection of techniques for improving the speed, accuracy, and memory requirements of end-to-end SPENs, and demonstrate the power of our method on 7-Scenes image denoising and CoNLL-2005 semantic role labeling tasks. In both, inexact minimization of non-convex SPEN energies is superior to baseline methods that use simplistic energy functions that can be minimized exactly.