Research
On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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0.3%0.5%0.8%0.1% · Jun 201819922001200920172026
3 results for “sequence-to-point”
Sequence to Point Learning Based on Bidirectional Dilated Residual Network for Non Intrusive Load Monitoringeess.SP

A new deep learning method for energy disaggregation.

problem Energy disaggregation or non-intrusive load monitoring (NILM) to identify individual appliance power usage.
method Sequence to Point Learning based on Bidirectional Dilated Residual Network (BRDN).
result Our method outperforms state-of-the-art approaches in all appliances on REDD and UK-DALE datasets.
NILMEnergy disaggregationDeep learningSequence to PointBidirectional Dilated Residual Network
2020-05-30abs ↗pdf ↗
Convolutional Sequence to Sequence Non-intrusive Load Monitoringstat.ML

A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network. The partiall…

2018-06-06abs ↗pdf ↗
Transfer Learning for Non-Intrusive Load Monitoringcs.LG

Transfer learning improves NILM across different appliance types and domains.

problem Recovering source appliances from mains data is challenging due to unidentifiability.
method Proposed two transfer learning schemes: appliance transfer learning and cross-domain transfer learning.
result Seq2point learning can be transferred across different domains with fine tuning required only for fully connected layers.
transfer learningNILMnon-intrusive load monitoringdeep learningsequence-to-point
2019-02-23abs ↗pdf ↗