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Found 8 papers, 0 papers with code
Changing agents and ascribing beliefs in dynamic epistemic logic
In dynamic epistemic logic (Van Ditmarsch, Van Der Hoek, & Kooi, 2008) it is customary to use an action frame (Baltag & Moss, 2004; Baltag, Moss, & Solecki, 1998) to describe different views of a single action.
Subjective Knowledge and Reasoning about Agents in Multi-Agent Systems
Though a lot of work in multi-agent systems is focused on reasoning about knowledge and beliefs of artificial agents, an explicit representation and reasoning about the presence/absence of agents, especially in the scenarios where agents may be unaware of other agents joining in or going offline in a multi-agent system, leading to partial knowledge/asymmetric knowledge of the agents is mostly overlooked by the MAS community.
Analysis Co-Sparse Coding for Energy Disaggregation
The advantage of our proposed approach is that, the requirement of training volume drastically reduces compared to state-of-the-art techniques.
Deep Sparse Coding for Non-Intrusive Load Monitoring
Prior studies in this area are shallow learning techniques, i. e. they learn a single layer of dictionary for every device.
Non-intrusive Load Monitoring via Multi-label Sparse Representation based Classification
This work follows the approach of multi-label classification for non-intrusive load monitoring (NILM).
Multi Label Restricted Boltzmann Machine for Non-Intrusive Load Monitoring
Some recent studies have proposed that if we frame Non-Intrusive Load Monitoring (NILM) as a multi-label classification problem, the need for appliance-level data can be avoided.
Deep Blind Compressed Sensing
In this work we show that by learning directly from the compressed domain, considerably better results can be obtained.
How to Train Your Deep Neural Network with Dictionary Learning
In the final layer one needs to use the label consistent dictionary learning formulation for classification.
