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Methods/Slanted Triangular Learning Rates

Slanted Triangular Learning Rates

GeneralIntroduced 200040 papers

Description

Slanted Triangular Learning Rates (STLR) is a learning rate schedule which first linearly increases the learning rate and then linearly decays it, which can be seen in Figure to the right. It is a modification of Triangular Learning Rates, with a short increase and a long decay period.

Papers Using This Method

Advanced Deep Learning Techniques for Analyzing Earnings Call Transcripts: Methodologies and Applications2025-02-27 No Argument Left Behind: Overlapping Chunks for Faster Processing of Arbitrarily Long Legal Texts2024-10-24 RICo: Reddit ideological communities2024-06-05 Exploring Multi-Level Threats in Telegram Data with AI-Human Annotation: A Preliminary Study2023-12-15 Illicit Darkweb Classification via Natural-language Processing: Classifying Illicit Content of Webpages based on Textual Information2023-12-08 Explainable and High-Performance Hate and Offensive Speech Detection2022-06-26 IIITT@Dravidian-CodeMix-FIRE2021: Transliterate or translate? Sentiment analysis of code-mixed text in Dravidian languages2021-11-15 Offensive Language Identification in Low-resourced Code-mixed Dravidian languages using Pseudo-labeling2021-08-27 Towards Offensive Language Identification for Tamil Code-Mixed YouTube Comments and Posts2021-08-24 Learning ULMFiT and Self-Distillation with Calibration for Medical Dialogue System2021-07-20 WHOSe Heritage: Classification of UNESCO World Heritage "Outstanding Universal Value" Documents with Soft Labels2021-04-12 L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset2021-03-21 Experimental Evaluation of Deep Learning models for Marathi Text Classification2021-01-13 LaDiff ULMFiT: A Layer Differentiated training approach for ULMFiT2021-01-13 HinglishNLP at SemEval-2020 Task 9: Fine-tuned Language Models for Hinglish Sentiment Detection2020-12-01 Smash at SemEval-2020 Task 7: Optimizing the Hyperparameters of ERNIE 2.0 for Humor Ranking and Rating2020-12-01 Palomino-Ochoa at SemEval-2020 Task 9: Robust System based on Transformer for Code-Mixed Sentiment Classification2020-11-18 Pagsusuri ng RNN-based Transfer Learning Technique sa Low-Resource Language2020-10-13 Gauravarora@HASOC-Dravidian-CodeMix-FIRE2020: Pre-training ULMFiT on Synthetically Generated Code-Mixed Data for Hate Speech Detection2020-10-05 Fine-tuning Pre-trained Contextual Embeddings for Citation Content Analysis in Scholarly Publication2020-09-12