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Papers

Long-term series forecasting with Query Selector -- efficient model of sparse attention

2021-07-19 · Jacek Klimek, Jakub Klimek, Witold Kraskiewicz, Mateusz Topolewski

Various modifications of TRANSFORMER were recently used to solve time-series forecasting problem. We propose Query Selector - an efficient, deterministic algorithm for sparse attention matrix. Experiments show it achieves state-of-the art results on ETT, Helpdesk and BPI'12 datasets.

📄 PDF Abstract BibTeX arXiv:2107.08687

Code (2)

moraieu/query-selector 공식 구현 pytorch
moraieu/query-selector-classification pytorch

Tasks

Multivariate Time Series ForecastingTime SeriesTime Series AnalysisTime Series Forecasting

Methods 이 논문이 사용한 방법론

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Absolute Position Encodings Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are…
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Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…
BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…
Adam 설명 없음

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