Salutary Labeling with Zero Human Annotation

27 May 2024

Papers citing "Salutary Labeling with Zero Human Annotation"

25 / 25 papers shown

Title
Characterizing the Influence of Graph Elements Zizhang Chen Peizhao Li Hongfu Liu Pengyu Hong TDI 28 22 0 14 Oct 2022
If Influence Functions are the Answer, Then What is the Question? Juhan Bae Nathan Ng Alston Lo Marzyeh Ghassemi Roger C. Grosse TDI 83 102 0 12 Sep 2022
Influence Selection for Active Learning Zhuoming Liu Hao Ding Huaping Zhong Weijia Li Jifeng Dai Conghui He TDI 60 94 0 20 Aug 2021
Deep Learning on a Data Diet: Finding Important Examples Early in Training Mansheej Paul Surya Ganguli Gintare Karolina Dziugaite 108 457 0 15 Jul 2021
A Survey of Deep Active Learning Pengzhen Ren Yun Xiao Xiaojun Chang Po-Yao (Bernie) Huang Zhihui Li Brij B. Gupta Xiaojiang Chen Xin Wang 95 1,139 0 30 Aug 2020
Learning from Noisy Labels with Deep Neural Networks: A Survey Hwanjun Song Minseok Kim Dongmin Park Yooju Shin Jae-Gil Lee NoLa 101 985 0 16 Jul 2020
Influence Functions in Deep Learning Are Fragile S. Basu Phillip E. Pope Soheil Feizi TDI 125 233 0 25 Jun 2020
Array Programming with NumPy Charles R. Harris K. Millman S. Walt R. Gommers Pauli Virtanen ... Tyler Reddy Warren Weckesser Hameer Abbasi C. Gohlke T. Oliphant 149 14,953 0 18 Jun 2020
Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions Xiaochuang Han Byron C. Wallace Yulia Tsvetkov MILM FAtt AAML TDI 70 172 0 14 May 2020
Estimating Training Data Influence by Tracing Gradient Descent G. Pruthi Frederick Liu Mukund Sundararajan Satyen Kale TDI 81 408 0 19 Feb 2020
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence Kihyuk Sohn David Berthelot Chun-Liang Li Zizhao Zhang Nicholas Carlini E. D. Cubuk Alexey Kurakin Han Zhang Colin Raffel AAML 155 3,549 0 21 Jan 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 493 42,407 0 03 Dec 2019
RoBERTa: A Robustly Optimized BERT Pretraining Approach Yinhan Liu Myle Ott Naman Goyal Jingfei Du Mandar Joshi Danqi Chen Omer Levy M. Lewis Luke Zettlemoyer Veselin Stoyanov AIMat 630 24,431 0 26 Jul 2019
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning Andreas Kirsch Joost R. van Amersfoort Y. Gal FedML 87 627 0 19 Jun 2019
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds Jordan T. Ash Chicheng Zhang A. Krishnamurthy John Langford Alekh Agarwal BDL UQCV 85 773 0 09 Jun 2019
On the Accuracy of Influence Functions for Measuring Group Effects Pang Wei Koh Kai-Siang Ang H. Teo Percy Liang TDI 73 191 0 30 May 2019
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels Pengfei Chen B. Liao Guangyong Chen Shengyu Zhang NoLa 67 386 0 13 May 2019
Learning Loss for Active Learning Donggeun Yoo In So Kweon UQCV 85 660 0 09 May 2019
Exploring Representativeness and Informativeness for Active Learning Bo Du Zengmao Wang Lefei Zhang Liangpei Zhang Wen Liu Jialie Shen Dacheng Tao 35 173 0 14 Apr 2019
Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems Bashir Rastegarpanah Krishna P. Gummadi M. Crovella 44 119 0 02 Dec 2018
Do Better ImageNet Models Transfer Better? Simon Kornblith Jonathon Shlens Quoc V. Le OOD MLT 155 1,327 0 23 May 2018
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 1.1K 7,154 0 20 Apr 2018
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 203 2,885 0 14 Mar 2017
Temporal Ensembling for Semi-Supervised Learning S. Laine Timo Aila UQCV 185 2,555 0 07 Oct 2016
An ensemble-based system for automatic screening of diabetic retinopathy B. Antal András Hajdu 85 237 0 30 Oct 2014