Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting

Pyraformer introduces a pyramidal attention mechanism that captures long-range dependencies in time-series data with linear time and space complexity, achieving state-of-the-art forecasting accuracy on multiple real-world datasets while reducing computational cost, as demonstrated in extensive ICLR-2022 experiments.

Deep LearningICLR 2022Pyraformer

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Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting

AntTech

Apr 27, 2022 · Artificial Intelligence

Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting

The paper introduces Pyraformer, a low‑complexity pyramidal‑attention Transformer that captures multi‑scale temporal dependencies with linear time‑space complexity, achieving superior single‑step and long‑range forecasting performance on real‑world datasets while supporting green‑computing capacity management.

PyraformerTransformerlong-range dependencies

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NetEase Smart Enterprise Tech+

Aug 23, 2021 · Artificial Intelligence

How a Lightweight Neural Network Cuts Transient Noise in Real‑Time Audio

NetEase Cloud Communication’s Audio Lab presents a low‑complexity neural‑network denoising algorithm that effectively suppresses both stationary and transient noises while preserving speech quality, detailing its mathematical model, feature design, loss function, GRU‑based architecture, real‑time performance, and comparative evaluation against state‑of‑the‑art methods.

Neural NetworkReal-time Processingaudio denoising

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How a Lightweight Neural Network Cuts Transient Noise in Real‑Time Audio