{"url":"/method/talk-convolutions","slug":"talk-convolutions","name":"TaLK Convolution","full_name":"Time-aware Large Kernel Convolution","full_name_withheld":false,"description_markdown":"A **Time-aware Large Kernel (TaLK) convolution** is a type of temporal [convolution](https://paperswithcode.com/method/convolution) that learns the kernel size of a summation kernel for each time-step instead of learning the kernel weights as in a typical convolution operation. For each time-step, a function is responsible for predicting the appropriate size of neighbor representations to use in the form of left and right offsets relative to the time-step.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Time-aware Large Kernel Convolutions","paper":"/paper/time-aware-large-kernel-convolutions","first_author":"Vasileios Lioutas","n_authors":2,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/time-aware-large-kernel-convolutions"},"source":{"url":"https://arxiv.org/abs/2002.03184v2","title":"Time-aware Large Kernel Convolutions","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Sequential","area_id":"sequential","collection":"Temporal Convolutions","url":"/methods/category/temporal-convolutions","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/time-aware-large-kernel-convolutions","title":"Time-aware Large Kernel Convolutions","date":"2020-02-08","arxiv_id":"2002.03184","n_code_links":1,"syntology":{"ran":5,"of":7,"unverified":2,"pointer_only":0}}],"papers_shown":1,"tasks":[{"task":"/task/document-summarization","name":"Document Summarization","papers":1},{"task":"/task/language-modeling","name":"Language Modeling","papers":1},{"task":"/task/language-modelling","name":"Language Modelling","papers":1},{"task":"/task/machine-translation","name":"Machine Translation","papers":1},{"task":"/task/translation","name":"Translation","papers":1}],"tasks_shown":5,"n_tasks":5,"usage_by_year":[{"year":"2020","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/talk-convolutions"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}