AI Paper Atlas

Research map / Learning theory and algorithms / Combinatorial and search optimization

Model-Free Iterative Temporal Appliance Discovery for Unsupervised Electricity Disaggregation

AAAI 2018 · Learning theory and algorithms › Combinatorial and search optimization · approach: Constrained neural network optimization · 5 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
expand episode search from length 2-3 to lengths 4 and 5 (ablation)Iterative discovery · REDDF-measureHelped
expand episode search to lengths 4 and 5 (ablation)Iterative discovery · BLUEDF-measureHurt
use ground truth events instead of Bayesian detected events (ablation)Both methods · BLUEDF-measureHelped
raise sampling rate of detected events from 1 Hz to 60 Hz (ablation)Both methods · BLUEDF-measureHurt
iterative discovery instead of modified motif miningAppliance discovery · REDDF-measureHelped
iterative discovery instead of modified motif miningAppliance discovery · BLUEDF-measureMixed
iterative discovery instead of modified motif miningEstimated energy per house · REDD houses H1-H6energy estimate accuracyMixed

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy