Fetching the paper…
Reading the bibliography…
Consider a prediction setting with few in-distribution labeled examples and many unlabeled examples both in- and out-of-distribution (OOD).
NDVI-derived land cover classifications at a global scale
R S DeFries and JRG Townshend · 1994
Earlier work this paper cites.
Global discrimination of land cover types from metrics derived from AVHRR pathfinder data
Ruth DeFries, Matthew Hansen, and John Townshend · 1995
Earlier work this paper cites.
Multitask learning
Rich Caruana · 1997
Earlier work this paper cites.
Benefitting from the variables that variable selection discards
Rich Caruana and Virginia R. de Sa · 2003
Earlier work this paper cites.
Random forests for land cover classification
Pall Oskar Gislason, Jon Atli Benediktsson, and Johannes R. Sveinsson · 2006
Earlier work this paper cites.
Land-cover change detection using multi-temporal MODIS NDVI data
Ross Lunetta, Joseph F Knight, Jayantha Ediriwickremaand John G Lyon, and L Dorsey Worthy · 2006
Earlier work this paper cites.
Domain adaptation of natural language processing systems
John Blitzer and Fernando Pereira · 2007
Earlier work this paper cites.
Frustratingly easy domain adaptation
Hal Daumé III · 2007
Earlier work this paper cites.
Estimating crop yield from multi-temporal satellite data using multivariate regression and neural network techniques
Ainong Li, Shunlin Liang, Angsheng Wang, and Jun Qin · 2007
Earlier work this paper cites.
Covariate shift adaptation by importance weighted cross validation
Masashi Sugiyama, Matthias Krauledat, and Klaus-Robert Muller · 2007
Earlier work this paper cites.
Estimating soil moisture using remote sensing data: A machine learning approach
Sajjad Ahmad, Ajay Kalra, and Haroon Stephen · 2010
Earlier work this paper cites.
Random design analysis of ridge regression
Daniel Hsu, Sham M. Kakade, and Tong Zhang · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Topics in random matrix theory
Terrence Tao · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K Simonyan and A. Zisserman · 2015
Earlier work this paper cites.
MOD09A1 MODIS/terra surface reflectance 8-day L3 global 500m SIN grid V006
E. Vermote · 2015
Earlier work this paper cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Francois Laviolette, Mario March, and Victor Lempitsky · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Combining satellite imagery and machine learning to predict poverty
Neal Jean, Marshall Burke, Michael Xie, W. Matthew Davis, David B. Lobell, and Stefano Ermon · 2016
Cited alongside, same era.
Crop yield forecasting on the canadian prairies by remotely sensed vegetation indices and machine learning methods
Michael D. Johnson, William W. Hsieh, Alex J. Cannon, Andrew Davidson, and Frédéric Bédard · 2016
Cited alongside, same era.
Machine learning in geosciences and remote sensing
David J. Lary, Amir H. Alavi, Amir H. Gandomi, and Annette L. Walker · 2016
Cited alongside, same era.
Data programming: Creating large training sets, quickly
Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, and Christopher Ré · 2016
Cited alongside, same era.
A survey of transfer learning
Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang · 2016
Cited alongside, same era.
Transfer learning from deep features for remote sensing and poverty mapping
1d convolutional neural networks and applications: A survey
Serkan Kiranyaz, Onur Avci, Osama Abdeljaber, Turker Ince, Moncef Gabbouj, and Daniel J Inman · 2019
Later among the works it cites.
Robustness to adversarial perturbations in learning from incomplete data
Amir Najafi, Shin ichi Maeda, Masanori Koyama, and Takeru Miyato · 2019
Later among the works it cites.
Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley · 2019
Later among the works it cites.
Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
Later among the works it cites.
Are labels required for improving adversarial robustness?
Jonathan Uesato, Jean-Baptiste Alayrac, Po-Sen Huang, Robert Stanforth, Alhussein Fawzi, and Pushmeet Kohli · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Michael Xie, Neal Jean, Marshall Burke, David Lobell, and Stefano Ermon · 2016
Cited alongside, same era.
ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate, 2017
C3S · 2017
Cited alongside, same era.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang · 2017
Cited alongside, same era.
Deep learning classification of land cover and crop types using remote sensing data
N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov · 2017
Cited alongside, same era.
Snorkel: Rapid training data creation with weak supervision
Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré · 2017
Cited alongside, same era.
Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing
EA AlBadawy, A Saha, and MA Mazurowski · 2018
Cited alongside, same era.
A high-performance and in-season classification system of field-level crop types using time-series landsat data and a machine learning approach
Yaping Cai, Kaiyu Guan, Jian Peng, Shaowen Wang, Christopher Seifert, Brian Wardlow, and Zhan Li · 2018
Cited alongside, same era.
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Closest in time.
Self-training avoids using spurious features under domain shift
Yining Chen, Colin Wei, Ananya Kumar, and Tengyu Ma · 2020
Closest in time.
Few-shot learning via learning the representation, provably
Simon S. Du, Wei Hu, Sham M. Kakade, Jason D. Lee, and Qi Lei · 2020
Closest in time.
Understanding self-training for gradual domain adaptation
Ananya Kumar, Tengyu Ma, and Percy Liang · 2020
Closest in time.
Understanding and mitigating the tradeoff between robustness and accuracy
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, and Percy Liang · 2020
Closest in time.
Meta-learning for few-shot land cover classification
Marc Rußwurm, Sherrie Wang, Marco Korner, and David Lobell · 2020
Closest in time.
Breeds: Benchmarks for subpopulation shift
Shibani Santurkar, Dimitris Tsipras, and Aleksander Madry · 2020
Closest in time.
Measuring robustness to natural distribution shifts in image classification
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt · 2020
Closest in time.
On the theory of transfer learning: The importance of task diversity
Nilesh Tripuraneni, Michael I. Jordan, and Chi Jin · 2020
Closest in time.
Weakly supervised deep learning for segmentation of remote sensing imagery
Sherrie Wang, William Chen, Sang Michael Xie, George Azzari, and David B. Lobell · 2020
Closest in time.
Understanding and improving information transfer in multi-task learning
Sen Wu, Hongyang R. Zhang, and Christopher Ré · 2020
Closest in time.
Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V. Le · 2020
Closest in time.
Using publicly available satellite imagery and deep learning to understand economic well-being in africa
Christopher Yeh, Anthony Perez, Anne Driscoll, George Azzari, Zhongyi Tang, David Lobell, Stefano Ermon, and Marshall Burke · 2020
Closest in time.
Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D. Cubuk, and Quoc V. Le · 2020
Closest in time.
Removing spurious features can hurt accuracy and affect groups disproportionately
Fereshte Khani and Percy Liang · 2021
Closest in time.