{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T11:28:22Z","timestamp":1750505302545,"version":"3.37.3"},"reference-count":31,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T00:00:00Z","timestamp":1701734400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T00:00:00Z","timestamp":1701734400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,5]]},"DOI":"10.1109\/bibm58861.2023.10385775","type":"proceedings-article","created":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T18:27:43Z","timestamp":1705602463000},"page":"602-608","source":"Crossref","is-referenced-by-count":1,"title":["Exploring Post-Training Quantization of Protein Language Models"],"prefix":"10.1109","author":[{"given":"Shuang","family":"Peng","sequence":"first","affiliation":[{"name":"Zhejiang Lab,Hangzhou,China"}]},{"given":"Fei","family":"Yang","sequence":"additional","affiliation":[{"name":"Zhejiang Lab,Hangzhou,China"}]},{"given":"Ning","family":"Sun","sequence":"additional","affiliation":[{"name":"Zhejiang Lab,Hangzhou,China"}]},{"given":"Sheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang Lab,Hangzhou,China"}]},{"given":"Yanfeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Nankai University,Tianjin,China"}]},{"given":"Aimin","family":"Pan","sequence":"additional","affiliation":[{"name":"Zhejiang Lab,Hangzhou,China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1126\/science.abj8754"},{"key":"ref2","first-page":"1405","article-title":"Towards efficient post-training quantization of pre-trained language models","volume-title":"Advances in Neural Information Processing Systems","volume":"35","author":"Bai"},{"key":"ref3","first-page":"7948","article-title":"Post training 4-bit quantization of convolutional networks for rapid-deployment","volume-title":"Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada","author":"Banner"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-021-01179-w"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.627"},{"key":"ref6","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Advances in neural information processing systems","volume":"33","author":"Brown"},{"key":"ref7","article-title":"PACT: parameterized clipping activation for quantized neural networks","volume":"abs\/1805.06085","author":"Choi","year":"2018","journal-title":"CoRR"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00363"},{"key":"ref9","doi-asserted-by":"crossref","DOI":"10.1101\/2021.11.09.467890","article-title":"Flip: Benchmark tasks in fitness landscape inference for proteins","volume-title":"Advances in Neural Information Processing Systems","author":"Dallago"},{"key":"ref10","article-title":"Protein complex prediction with alphafold-multimer","author":"Evans","year":"2021","journal-title":"BioRxiv"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_5"},{"key":"ref12","doi-asserted-by":"crossref","DOI":"10.21203\/rs.3.rs-1969991\/v1","article-title":"Helixfold-single: Msa-free protein structure prediction by using protein language model as an alternative","author":"Fang","year":"2022"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1002\/prot.25431"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cels.2022.01.003"},{"article-title":"Exploring evolution-aware &-free protein language models as protein function predictors","year":"2022","author":"Hu","key":"ref15"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03819-2"},{"key":"ref17","first-page":"4171","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proceedings of NAACL-HLT","author":"Kenton"},{"article-title":"I-bert: Integer-only bert quantization","volume-title":"International Conference on Machine Learning (Accepted)","author":"Kim","key":"ref18"},{"article-title":"Quantizing deep convolutional networks for efficient inference: A whitepaper","year":"2018","author":"Krishnamoorthi","key":"ref19"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1002\/prot.26237"},{"key":"ref21","article-title":"Language models of protein sequences at the scale of evolution enable accurate structure prediction","author":"Lin","year":"2022","journal-title":"bioRxiv"},{"article-title":"A white paper on neural network quantization","year":"2021","author":"Nagel","key":"ref22"},{"journal-title":"Improving language understanding by generative pre-training","year":"2018","author":"Radford","key":"ref23"},{"key":"ref24","article-title":"Evaluating protein transfer learning with tape","volume-title":"Advances in neural information processing systems","volume":"32","author":"Rao"},{"key":"ref25","doi-asserted-by":"crossref","DOI":"10.1101\/2020.12.15.422761","article-title":"Transformer protein language models are unsupervised structure learners","volume-title":"International Conference on Learning Representations","author":"Rao"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2016239118"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2016239118"},{"key":"ref28","article-title":"Attention is all you need","volume-title":"Advances in neural information processing systems","volume":"30","author":"Vaswani"},{"key":"ref29","article-title":"High-resolution de novo structure prediction from primary sequence","author":"Wu","year":"2022","journal-title":"bioRxiv"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19775-8_12"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gki524"}],"event":{"name":"2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","start":{"date-parts":[[2023,12,5]]},"location":"Istanbul, Turkiye","end":{"date-parts":[[2023,12,8]]}},"container-title":["2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10385250\/10385251\/10385775.pdf?arnumber=10385775","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T17:48:15Z","timestamp":1729100895000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10385775\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,5]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/bibm58861.2023.10385775","relation":{},"subject":[],"published":{"date-parts":[[2023,12,5]]}}}