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We present a method to compute the derivative of a learning task with respect to a dataset.
The monte carlo method
Nicholas Metropolis and Stanislaw Ulam · 1949
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Peter J Green and Bernard W Silverman · 1993
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Locally weighted learning
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Lazy learning meets the recursive least squares algorithm
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Identifying mislabeled training data
Carla E Brodley and Mark A Friedl · 1999
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Rethinking the hyperparameters for fine-tuning
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Leave-one-out cross-validation based model selection criteria for weighted ls-svms
Gavin C Cawley · 2006
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Caltech-256 object category dataset
Gregory Griffin, Alex Holub, and Pietro Perona · 2007
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A kernel-based two-class classifier for imbalanced data sets
Xia Hong, Sheng Chen, and Chris J Harris · 2007
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Notes on regularized least squares
Ryan M Rifkin and Ross A Lippert · 2007
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Monte Carlo strategies in scientific computing
Jun S Liu · 2008
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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A heuristic weight-setting strategy and iteratively updating algorithm for weighted least-squares support vector regression
Wen Wen, Zhifeng Hao, and Xiaowei Yang · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Recognizing indoor scenes
Ariadna Quattoni and Antonio Torralba · 2009
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On over-fitting in model selection and subsequent selection bias in performance evaluation
Gavin C Cawley and Nicola LC Talbot · 2010
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Weighted least squares support vector machine local region method for nonlinear time series prediction
Tingwei Quan, Xiaomao Liu, and Qian Liu · 2010
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Caltech-UCSD Birds 200
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Scikit-learn: Machine learning in python
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
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Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Active bias: Training more accurate neural networks by emphasizing high variance samples
Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum · 2017
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Class rectification hard mining for imbalanced deep learning
Qi Dong, Shaogang Gong, and Xiatian Zhu · 2017
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Auto-sklearn: efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, and Frank Hutter · 2019
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Deep imbalanced learning for face recognition and attribute prediction
Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang · 2019
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Gradient harmonized single-stage detector
Buyu Li, Yu Liu, and Xiaogang Wang · 2019
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Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, and Sungwoong Kim · 2019
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Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation
Chenxi Liu, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Wei Hua, Alan L Yuille, and Li Fei-Fei · 2019
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Geographically weighted least squares-support vector machine
Changha Hwang and Jooyong Shim · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Malicious software classification using transfer learning of resnet-50 deep neural network
Edmar Rezende, Guilherme Ruppert, Tiago Carvalho, Fabio Ramos, and Paulo De Geus · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
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Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer · 2019
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Hard sample mining for the improved retraining of automatic speech recognition
Jiabin Xue, Jiqing Han, Tieran Zheng, Jiaxing Guo, and Boyong Wu · 2019
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Lqf: Linear quadratic fine-tuning
Alessandro Achille, Aditya Golatkar, Avinash Ravichandran, Marzia Polito, and Stefano Soatto · 2020
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Autosimulate:(quickly) learning synthetic data generation
Harkirat Singh Behl, Atilim Güneş Baydin, Ran Gal, Philip HS Torr, and Vibhav Vineet · 2020
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Hypernetwork-based augmentation
Chih-Yang Chen, Che-Han Chang, and Edward Y Chang · 2020
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Data distribution search to select core-set for machine learning
Myunggwon Hwang, Yuna Jeong, and Wonkyung Sung · 2020
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Dataset distillation for core training set construction
Yuna Jeong, Myunggwon Hwang, and Wonkyung Sung · 2020
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Select to better learn: Fast and accurate deep learning using data selection from nonlinear manifolds
Mohsen Joneidi, Saeed Vahidian, Ashkan Esmaeili, Weijia Wang, Nazanin Rahnavard, Bill Lin, and Mubarak Shah · 2020
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Gradients as features for deep representation learning
Fangzhou Mu, Yingyu Liang, and Yin Li · 2020
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Estimating training data influence by tracking gradient descent
Garima Pruthi, Frederick Liu, Mukund Sundararajan, and Satyen Kale · 2020
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Adaptive weighted least-squares polynomial chaos expansion with basis adaptivity and sequential adaptive sampling
Mishal Thapa, Sameer B Mulani, and Robert W Walters · 2020
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Neural data server: A large-scale search engine for transfer learning data
Xi Yan, David Acuna, and Sanja Fidler · 2020
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A linearized framework and a new benchmark for model selection for fine-tuning
Aditya Deshpande, Alessandro Achille, Avinash Ravichandran, Hao Li, Luca Zancato, Charless Fowlkes, Rahul Bhotika, Stefano Soatto, and Pietro Perona · 2021
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Automl: A survey of the state-of-the-art
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GRAD-MATCH: A gradient matching based data subset selection for efficient learning
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Direct differentiable augmentation search
Aoming Liu, Zehao Huang, Zhiwu Huang, and Naiyan Wang · 2021
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Outlier detection algorithms over fuzzy data with weighted least squares
Natalia Nikolova, Rosa M Rodríguez, Mark Symes, Daniela Toneva, Krasimir Kolev, and Kiril Tenekedjiev · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Understanding the role of importance weighting for deep learning
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