Biased Importance Sampling for Deep Neural Network Training

31 May 2017

Papers citing "Biased Importance Sampling for Deep Neural Network Training"

17 / 17 papers shown

Title
Randomized Pairwise Learning with Adaptive Sampling: A PAC-Bayes Analysis Sijia Zhou Yunwen Lei Ata Kabán 36 0 0 03 Apr 2025
Multiple Importance Sampling for Stochastic Gradient Estimation Corentin Salaün Xingchang Huang Iliyan Georgiev Niloy J. Mitra Gurprit Singh 32 1 0 22 Jul 2024
A Negative Result on Gradient Matching for Selective Backprop Lukas Balles Cédric Archambeau Giovanni Zappella 47 0 0 08 Dec 2023
REDUCR: Robust Data Downsampling Using Class Priority Reweighting William Bankes George Hughes Ilija Bogunovic Zi Wang 38 3 0 01 Dec 2023
$Computing Approximate $\ell_p$ Sensitivities$ Computing Approximate $\ell_p$ Sensitivities Swati Padmanabhan David P. Woodruff Qiuyi Zhang 58 0 0 07 Nov 2023
On Efficient Training of Large-Scale Deep Learning Models: A Literature Review Li Shen Yan Sun Zhiyuan Yu Liang Ding Xinmei Tian Dacheng Tao VLM 35 41 0 07 Apr 2023
Rank-based Decomposable Losses in Machine Learning: A Survey Shu Hu Xin Wang Siwei Lyu 40 32 0 18 Jul 2022
Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt Sören Mindermann J. Brauner Muhammed Razzak Mrinank Sharma Andreas Kirsch ... Benedikt Höltgen Aidan Gomez Adrien Morisot Sebastian Farquhar Y. Gal 67 152 0 14 Jun 2022
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection Shaoshen Wang Yanbin Liu Ling Chen Chengqi Zhang 24 0 0 29 May 2022
DELTA: Diverse Client Sampling for Fasting Federated Learning Lung-Chuang Wang Yongxin Guo Tao R. Lin Xiaoying Tang FedML 28 23 0 27 May 2022
Observations on K-image Expansion of Image-Mixing Augmentation for Classification Joonhyun Jeong Sungmin Cha Jongwon Choi Sangdoo Yun Taesup Moon Y. Yoo VLM 28 6 0 08 Oct 2021
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 75 225 0 26 Apr 2021
Adaptive Task Sampling for Meta-Learning Chenghao Liu Zhihao Wang Doyen Sahoo Yuan Fang Kun Zhang Guosheng Lin 45 54 0 17 Jul 2020
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets Ilqar Ramazanli Han Nguyen Hai Pham Sashank J. Reddi Barnabás Póczós 23 11 0 20 Feb 2020
Rethinking deep active learning: Using unlabeled data at model training Oriane Siméoni Mateusz Budnik Yannis Avrithis G. Gravier HAI 30 79 0 19 Nov 2019
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications Nicholas Carlini Ulfar Erlingsson Nicolas Papernot OOD OODD 26 62 0 29 Oct 2019
Submodular Batch Selection for Training Deep Neural Networks K. J. Joseph R. VamshiTeja Krishnakant Singh V. Balasubramanian 15 23 0 20 Jun 2019