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Found 5 papers, 2 papers with code
Variation-based Cause Effect Identification
In the causal direction, such variations are expected to have no impact on the effect generation mechanism.
Multi-level Attention Model for Weakly Supervised Audio Classification
The objective of audio classification is to predict the presence or absence of audio events in an audio clip.
Neural Network Ensembles to Real-time Identification of Plug-level Appliance Measurements
The problem of identifying end-use electrical appliances from their individual consumption profiles, known as the appliance identification problem, is a primary stage in both Non-Intrusive Load Monitoring (NILM) and automated plug-wise metering.
On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction
Recently, and with the growing development of big energy datasets, data-driven learning techniques began to represent a potential solution to the energy disaggregation problem outperforming engineered and hand-crafted models.
Selective Sampling and Mixture Models in Generative Adversarial Networks
In this paper, we propose a multi-generator extension to the adversarial training framework, in which the objective of each generator is to represent a unique component of a target mixture distribution.
