12 citations · 25 across the 7 of their papers we have counts for
7 papers
Exploring Bayesian Surprise to Prevent Overfitting and to Predict Model Performance in Non-Intrusive Load Monitoring
Richard Jones, Christoph Klemenjak, Stephen Makonin +1
Non-Intrusive Load Monitoring (NILM) is a field of research focused on segregating constituent electrical loads in a system based only on their aggregated signal. Significant compu…
PowerGAN: Synthesizing Appliance Power Signatures Using Generative Adversarial Networks
Alon Harell, Richard Jones, Stephen Makonin +1
Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into home appliance electricity consumption using only the building's smart meter. Most curre…
Shared Mobile-Cloud Inference for Collaborative Intelligence
Mateen Ulhaq, Ivan V. Bajić
As AI applications for mobile devices become more prevalent, there is an increasing need for faster execution and lower energy consumption for neural model inference. Historically,…
Towards Automated Swimming Analytics Using Deep Neural Networks
Timothy Woinoski, Alon Harell, Ivan V. Bajic
Methods for creating a system to automate the collection of swimming analytics on a pool-wide scale are considered in this paper. There has not been much work on swimmer tracking o…
FDDB-360: Face Detection in 360-degree Fisheye Images
Jianglin Fu, Saeed Ranjbar Alvar, Ivan V. Bajic +1
360-degree cameras offer the possibility to cover a large area, for example an entire room, without using multiple distributed vision sensors. However, geometric distortions introd…
Wavenilm: A causal neural network for power disaggregation from the complex power signal
Alon Harell, Stephen Makonin, Ivan V. Bajić
Non-intrusive load monitoring (NILM) helps meet energy conservation goals by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks h…