{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/polla-enhancing-the-local-structure-awareness","title":"POLLA: Enhancing the Local Structure Awareness in Long Sequence Spatial-temporal Modeling","arxiv_id":null,"date":"2021-11-29","proceeding":"TIST 2021 2021 11","authors":["Haoyi Zhou","Hao Peng","Jieqi Peng","Shuai Zhang","JianXin Li"],"abstract":"The spatial-temporal modeling on long sequences is of great importance in many real-world applications.\r\nRecent studies have shown the potential of applying the self-attention mechanism to improve capturing\r\nthe complex spatial-temporal dependencies. However, the lack of underlying structure information weakens its general performance on long sequence spatial-temporal problem. To overcome this limitation, we\r\nproposed a novel method, named the Proximity-aware Long Sequence Learning framework, and apply it to\r\nthe spatial-temporal forecasting task. The model substitutes the canonical self-attention by leveraging the\r\nproximity-aware attention, which enhances local structure clues in building long-range dependencies with\r\na linear approximation of attention scores. The relief adjacency matrix technique can utilize the historical\r\nglobal graph information for consistent proximity learning. Meanwhile, the reduced decoder allows for fast\r\ninference in a non-autoregressive manner. Extensive experiments are conducted on five large-scale datasets,\r\nwhich demonstrate that our method achieves state-of-the-art performance and validates the effectiveness\r\nbrought by local structure information.","url_abs":"https://dl.acm.org/doi/pdf/10.1145/3447987","url_pdf":"https://dl.acm.org/doi/pdf/10.1145/3447987","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"polla-enhancing-the-local-structure-awareness","repo_url":"https://github.com/zhouhaoyi/POLLA","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}