Feb. 9, 2024, 5:43 a.m. | Yanjun Zhao Tian Zhou Chao Chen Liang Sun Yi Qian Rong Jin

cs.LG updates on arXiv.org arxiv.org

Time series analysis is vital for numerous applications, and transformers have become increasingly prominent in this domain. Leading methods customize the transformer architecture from NLP and CV, utilizing a patching technique to convert continuous signals into segments. Yet, time series data are uniquely challenging due to significant distribution shifts and intrinsic noise levels. To address these two challenges,we introduce the Sparse Vector Quantized FFN-Free Transformer (Sparse-VQ). Our methodology capitalizes on a sparse vector quantization technique coupled with Reverse Instance Normalization …

analysis applications architecture become continuous cs.ai cs.lg data domain forecasting framework free nlp quantization series time series time series forecasting transformer transformer architecture transformers vector vital

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

ETL Developer

@ Gainwell Technologies | Bengaluru, KA, IN, 560100

Medical Radiation Technologist, Breast Imaging

@ University Health Network | Toronto, ON, Canada

Data Scientist

@ PayPal | USA - Texas - Austin - Corp - Alterra Pkwy