Search Results for author:
Found 8 papers, 4 papers with code
SnapperGPS: Open Hardware for Energy-Efficient, Low-Cost Wildlife Location Tracking with Snapshot GNSS
This is ideal for low-power applications such as animal tracking.
Replication-Robust Payoff-Allocation for Machine Learning Data Markets
In this paper, we systematically study the replication manipulation in submodular games and investigate replication robustness, a metric that quantitatively measures the robustness of solution concepts against replication.
Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination
We evaluate our model empirically on a set of multi-agent pursuit and taxi tasks, and show that our agents learn to adapt flexibly across scenarios that require different termination behaviours.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Demo Abstract: NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets
In this demonstration, we present an open source toolkit for evaluating non-intrusive load monitoring research; a field which aims to disaggregate a household's total electricity consumption into individual appliances.
Other Computer Science
NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring
We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.
Applications
Efficient State-Space Inference of Periodic Latent Force Models
We apply our approach to model the thermal dynamics of domestic buildings and show that it is effective at predicting day-ahead temperatures within the homes.
Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries
In many developing countries, half the population lives in rural locations, where access to essentials such as school materials, mosquito nets, and medical supplies is restricted.
Bounding the Estimation Error of Sampling-based Shapley Value Approximation
While this algorithm provides a bound on the approximation error, this bound is \textit{asymptotic}, meaning that it only holds when the number of samples increases to infinity.
Computer Science and Game Theory
