Approximating Instance-Dependent Noise via Instance-Confidence Embedding

25 March 2021

Papers citing "Approximating Instance-Dependent Noise via Instance-Confidence Embedding"

5 / 5 papers shown

Title
An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Jingyi Cui Yi-Ge Zhang Hengyu Liu Yisen Wang NoLa 48 0 0 03 Jan 2025
Training Dynamic based data filtering may not work for NLP datasets Arka Talukdar Monika Dagar Prachi Gupta Varun G. Menon NoLa 48 3 0 19 Sep 2021
Curriculum Loss: Robust Learning and Generalization against Label Corruption Yueming Lyu Ivor W. Tsang NoLa 63 172 0 24 May 2019
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 299 6,984 0 20 Apr 2018
Efficient Estimation of Word Representations in Vector Space Tomáš Mikolov Kai Chen G. Corrado J. Dean 3DV 281 31,267 0 16 Jan 2013