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Found 4 papers, 0 papers with code
Machine Learning-Based Soft Sensors for Vacuum Distillation Unit
Firstly, it is important to enhance the quality of both sets of data (laboratory measurements and physical sensors) in a data pre-processing stage (as described in Methodology section).
Pre-treatment of outliers and anomalies in plant data: Methodology and case study of a Vacuum Distillation Unit
In this case, we used principal component analysis (PCA) with Hotelling's $T^2$ statistics to identify the long-term outliers.
Visual Question Answering as a Multi-Task Problem
Visual Question Answering(VQA) is a highly complex problem set, relying on many sub-problems to produce reasonable answers.
Eliminating Catastrophic Interference with Biased Competition
We create and exhibit a new toy dataset, based on the MNIST dataset, which we call MNIST-QA, for testing Visual Question Answering architectures in a low-dimensional environment while preserving the more difficult components of the Visual Question Answering task, and demonstrate the proposed network architecture on this new dataset, as well as on COCO-QA and DAQUAR-FULL.
