{"url":"/method/1cycle","slug":"1cycle","name":"1cycle","full_name":"1cycle learning rate scheduling policy","full_name_withheld":false,"description_markdown":null,"description_state":"absent","introduced_year":null,"introduced_by":{"title":"A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay","paper":"/paper/a-disciplined-approach-to-neural-network","first_author":"Leslie N. Smith","n_authors":1,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/a-disciplined-approach-to-neural-network"},"source":{"url":"http://arxiv.org/abs/1803.09820v2","title":"A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Learning Rate Schedules","url":"/methods/category/learning-rate-schedules","pwc_aliases":[]}],"n_papers_tagged":13,"archive_num_papers":13,"papers_newest_first":[{"paper":"/paper/learning-minimal-representations-of","title":"Learning minimal representations of stochastic processes with variational autoencoders","date":"2023-07-21","arxiv_id":"2307.11608","n_code_links":1,"syntology":null},{"paper":"/paper/baize-an-open-source-chat-model-with","title":"Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data","date":"2023-04-03","arxiv_id":"2304.01196","n_code_links":5,"syntology":{"ran":1,"of":9,"unverified":8,"pointer_only":1}},{"paper":"/paper/nuwa-visual-synthesis-pre-training-for-neural","title":"NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion","date":"2021-11-24","arxiv_id":"2111.12417","n_code_links":1,"syntology":{"ran":3,"of":3,"unverified":0,"pointer_only":2}},{"paper":"/paper/adop-approximate-differentiable-one-pixel","title":"ADOP: Approximate Differentiable One-Pixel Point Rendering","date":"2021-10-13","arxiv_id":"2110.06635","n_code_links":2,"syntology":null},{"paper":"/paper/smart-contract-vulnerability-detection-from","title":"Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion","date":"2021-06-17","arxiv_id":"2106.09282","n_code_links":1,"syntology":{"ran":0,"of":2,"unverified":2,"pointer_only":2}},{"paper":"/paper/self-attention-between-datapoints-going","title":"Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning","date":"2021-06-04","arxiv_id":"2106.02584","n_code_links":3,"syntology":{"ran":10,"of":30,"unverified":20,"pointer_only":0}},{"paper":"/paper/convnets-for-counting-object-detection-of","title":"ConvNets for Counting: Object Detection of Transient Phenomena in Steelpan Drums","date":"2021-02-01","arxiv_id":"2102.00632","n_code_links":1,"syntology":null},{"paper":"/paper/improving-polyphonic-music-models-with","title":"Improving Polyphonic Music Models with Feature-Rich Encoding","date":"2019-11-26","arxiv_id":"1911.11775","n_code_links":2,"syntology":null},{"paper":"/paper/a-disciplined-approach-to-neural-network","title":"A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay","date":"2018-03-26","arxiv_id":"1803.09820","n_code_links":30,"syntology":{"ran":3,"of":7,"unverified":4,"pointer_only":2}},{"paper":"/paper/using-trusted-data-to-train-deep-networks-on","title":"Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise","date":"2018-02-14","arxiv_id":"1802.05300","n_code_links":1,"syntology":{"ran":0,"of":6,"unverified":6,"pointer_only":0}},{"paper":"/paper/sgpn-similarity-group-proposal-network-for-3d","title":"SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation","date":"2017-11-23","arxiv_id":"1711.08588","n_code_links":1,"syntology":null},{"paper":"/paper/time-contrastive-networks-self-supervised","title":"Time-Contrastive Networks: Self-Supervised Learning from Video","date":"2017-04-23","arxiv_id":"1704.06888","n_code_links":7,"syntology":null},{"paper":null,"title":"Large-Scale Music Annotation and Retrieval: Learning to Rank in Joint Semantic Spaces","date":"2011-05-26","arxiv_id":"1105.5196","n_code_links":0,"syntology":null}],"papers_shown":13,"tasks":[{"task":"/task/3d-part-segmentation","name":"3D Part Segmentation","papers":2},{"task":"/task/deep-learning","name":"Deep Learning","papers":2},{"task":"/task/object-detection","name":"Object Detection","papers":2},{"task":"/task/object-detection-1","name":"object-detection","papers":2},{"task":"/task/3d-instance-segmentation-1","name":"3D Instance Segmentation","papers":1},{"task":"/task/3d-object-detection","name":"3D Object Detection","papers":1},{"task":"/task/3d-semantic-instance-segmentation","name":"3D Semantic Instance Segmentation","papers":1},{"task":"/task/chatbot","name":"Chatbot","papers":1},{"task":"/task/data-poisoning","name":"Data Poisoning","papers":1},{"task":"/task/decoder","name":"Decoder","papers":1},{"task":null,"name":"Generative Adversarial Network","papers":1},{"task":"/task/image-generation","name":"Image Generation","papers":1},{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":1},{"task":"/task/inverse-rendering","name":"Inverse Rendering","papers":1},{"task":"/task/language-modeling","name":"Language Modeling","papers":1},{"task":"/task/language-modelling","name":"Language Modelling","papers":1},{"task":"/task/large-language-model","name":"Large Language Model","papers":1},{"task":"/task/learning-to-rank","name":"Learning-To-Rank","papers":1},{"task":"/task/metric-learning","name":"Metric Learning","papers":1},{"task":"/task/multi-task-learning","name":"Multi-Task Learning","papers":1}],"tasks_shown":20,"n_tasks":43,"usage_by_year":[{"year":"2011","papers":1},{"year":"2017","papers":2},{"year":"2018","papers":2},{"year":"2019","papers":1},{"year":"2021","papers":5},{"year":"2023","papers":2}],"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/1cycle"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}