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Distribution shift occurs when the test distribution differs from the training distribution, and it can considerably degrade performance of machine learning models deployed in the real world.
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When google got flu wrong: Us outbreak foxes a leading web-based method for tracking seasonal flu
Declan Butler · 2013
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Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
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High-resolution global maps of 21st-century forest cover change
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3d object representations for fine-grained categorization
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Fine-grained visual classification of aircraft
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Domain adaptation for sentiment classification in light of multiple sources
Fang Fang, Kaushik Dutta, and Anindya Datta · 2014
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Fostering innovation, creating jobs, driving better decisions: The value of government data
R Powers and D Beede · 2014
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Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
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A century of portraits: A visual historical record of american high school yearbooks
Shiry Ginosar, Kate Rakelly, Sarah Sachs, Brian Yin, and Alexei A Efros · 2015
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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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
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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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Patient subtyping via time-aware lstm networks
Inci M Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K Jain, and Jiayu Zhou · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks
Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, and Jing Gao · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Mapping the world population one building at a time
Tobias G. Tiecke, Xianming Liu, Amy Zhang, Andreas Gros, Nan Li, Gregory Yetman, Talip Kilic, Siobhan Murray, Brian Blankespoor, Espen B. Prydz, and Hai-Anh H. Dang · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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There are many consistent explanations of unlabeled data: Why you should average
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson · 2018
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Ethical considerations when using geospatial technologies for evidence generation
Gabrielle Berman, Sara de la Rosa, Tanya Accone, et al · 2018
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Deep learning and virtual drug screening
Kristy A Carpenter, David S Cohen, Juliet T Jarrell, and Xudong Huang · 2018
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Functional map of the world
Breeds: Benchmarks for subpopulation shift
Shibani Santurkar, Dimitris Tsipras, and Aleksander Madry · 2020
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Weakly supervised deep learning for segmentation of remote sensing imagery
Sherrie Wang, William Chen, Sang Michael Xie, George Azzari, and David B Lobell · 2020
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Adversarial domain adaptation with domain mixup
Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang · 2020
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Deep domain-adversarial image generation for domain generalisation
Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, and Tao Xiang · 2020
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Invariance principle meets information bottleneck for out-of-distribution generalization
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
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Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot · 2018
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News category dataset, 06 2018
Rishabh Misra · 2018
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Deepdta: deep drug–target binding affinity prediction
Hakime Öztürk, Arzucan Özgür, and Elif Ozkirimli · 2018
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Do cifar-10 classifiers generalize to cifar-10?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2018
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Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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Online continual learning with natural distribution shifts: An empirical study with visual data
Zhipeng Cai, Ozan Sener, and Vladlen Koltun · 2021
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Towards non-iid image classification: A dataset and baselines
Yue He, Zheyan Shen, and Peng Cui · 2021
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Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik · 2021
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The power of contrast for feature learning: A theoretical analysis
Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, and Linjun Zhang · 2021
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Lifelong pretraining: Continually adapting language models to emerging corpora
Xisen Jin, Dejiao Zhang, Henghui Zhu, Wei Xiao, Shang-Wen Li, Xiaokai Wei, Andrew Arnold, and Xiang Ren · 2021
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Mimic-iv, 2021
Alistair Johnson, Lucas Bulgarelli, Tom Pollard, Steven Horng, Leo Anthony Celi, and Roger Mark · 2021
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Pytorch tabular: A framework for deep learning with tabular data, 2021
Manu Joseph · 2021
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Prakhar Kaushik, Alex Gain, Adam Kortylewski, and Alan Yuille · 2021
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Adapting bert for continual learning of a sequence of aspect sentiment classification tasks
Zixuan Ke, Hu Xu, and Bing Liu · 2021
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On invariance penalties for risk minimization
Kia Khezeli, Arno Blaas, Frank Soboczenski, Nicholas Chia, and John Kalantari · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, et al · 2021
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Out-of-distribution generalization via risk extrapolation (rex)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville · 2021
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Mind the gap: Assessing temporal generalization in neural language models
Angeliki Lazaridou, Adhi Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d’Autume, Tomas Kocisky, Sebastian Ruder, et al · 2021
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The clear benchmark: Continual learning on real-world imagery
Zhiqiu Lin, Jia Shi, Deepak Pathak, and Deva Ramanan · 2021
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Shifts: A dataset of real distributional shift across multiple large-scale tasks
Andrey Malinin, Neil Band, German Chesnokov, Yarin Gal, Mark JF Gales, Alexey Noskov, Andrey Ploskonosov, Liudmila Prokhorenkova, Ivan Provilkov, Vatsal Raina, et al · 2021
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
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Sculpting Data for ML: The first act of Machine Learning
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Yield prediction with machine learning algorithms and satellite images
Alireza Sharifi · 2021
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Change matters: Medication change prediction with recurrent residual networks
Chaoqi Yang, Cao Xiao, Lucas Glass, and Jimeng Sun · 2021
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Coping with label shift via distributionally robust optimisation
Jingzhao Zhang, Aditya Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, and Suvrit Sra · 2021
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Examining and combating spurious features under distribution shift
Chunting Zhou, Xuezhe Ma, Paul Michel, and Graham Neubig · 2021
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Class-incremental continual learning into the extended der-verse
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The polarization in today’s congress has roots that go back decades
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Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine
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