Domain Adaptation for Statistical Classifiers

Maximum Likelihood from Incomplete Data Via the EM Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444

Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935

Multitask Learning

Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636

Hierarchical Mixtures of Experts and the EM Algorithm

Jordan, M. I., & Jacobs, R. A. (1994) · Neural Computation · doi:10.1162/neco.1994.6.2.181 · Cited by 2,786

An extensive empirical study of feature selection metrics for text classification

Forman, G. (2003) · doi:10.5555/944919.944974 · Cited by 2,400

Nonparametric Statistical Inference

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Maximum Entropy Markov Models for Information Extraction and Segmentation

McCallum, A., Freitag, D., & Pereira, F. C. N. (2000) · Cited by 1,321

Inducing features of random fields

Pietra, S. D., Pietra, V. D., & Lafferty, J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.588021 · Cited by 1,034

Multitask Learning: A Knowledge-Based Source of Inductive Bias

Caruana, R. (1993) · Elsevier eBooks · doi:10.1016/b978-1-55860-307-3.50012-5 · Cited by 753

A Model of Inductive Bias Learning

Baxter, J. (2000) · Cited by 730

Learning to Adapt Web Information Extraction Knowledge and Discovering New Attributes via a Bayesian Approach

Wong, T.-L., & Lam, W. P. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.111 · Cited by 44

Contrastive Language Adaptation for Cross-Lingual Stance Detection

Mohtarami, M., Glass, J. R., & Nakov, P. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1452 · Cited by 46

Text categorization with knowledge transfer from heterogeneous data sources

Gupta, R. K., & Ratinov, L. (2008) · Cited by 44

Transportability from Multiple Environments with Limited Experiments: Completeness Results

Bareinboim, E., & Pearl, J. (2014) · Cited by 43

Building a General Purpose Cross-Domain Sentiment Mining Model

Whitehead, M. T., & Yaeger, L. (2009) · doi:10.1109/csie.2009.754 · Cited by 42

Unsupervised Domain Adaptation for Joint Segmentation and POS-Tagging

Liu, Y., & Zhang, Y. (2012) · Cited by 41

Robust, Lexicalized Native Language Identification

Brooke, J., & Hirst, G. (2012) · Cited by 41

MEG decoding across subjects

Olivetti, E., Kia, S. M., & Avesani, P. (2014) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2014.6858538 · Cited by 41

Exploiting Feature Hierarchy for Transfer Learning in Named Entity Recognition

Arnold, A. O., Nallapati, R. M., & Cohen, W. W. (2008) · Cited by 42

Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media

Park, C. Y., Mendelsohn, J., Field, A., & Tsvetkov, Y. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.382 · Cited by 66

A Novel Graph-Matching-Based Approach for Domain Adaptation in Classification of Remote Sensing Image Pair

Banerjee, B., Bovolo, F., Bhattacharya, A., Bruzzone, L., Chaudhuri, S., & Buddhiraju, K. M. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2389520 · Cited by 40

Ranking model adaptation for domain-specific search

Geng, B., Yang, L., Xu, C., & Hua, X. (2009) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1645953.1645980 · Cited by 40

Geospatial Correspondences for Multimodal Registration

Marcos, D., Hamid, R., & Tuia, D. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.550 · Cited by 40

Domain-Adapted Convolutional Networks for Satellite Image Classification: A Large-Scale Interactive Learning Workflow

Lunga, D., Yang, H. L., Reith, A., Weaver, J. E., Yuan, J., & Bhaduri, B. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2795753 · Cited by 40

Air quality estimation by exploiting terrain features and multi-view transfer semi-supervised regression

Lv, M., Li, Y., Chen, L., & Chen, T. (2019) · Information Sciences · doi:10.1016/j.ins.2019.01.038 · Cited by 40

Explainable Deep Classification Models for Domain Generalization

Zunino, A., Bargal, S. A., Volpi, R., Sameki, M., Zhang, J., Sclaroff, S., Murino, V., & Saenko, K. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00361 · Cited by 44

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

Jamshidi, E., Asgary, A., Tavakoli, N., Zali, A., Setareh, S., Esmaily, H., Jamaldini, S. H., Daaee, A., Babajani, A., Kashi, M. A. S., et al. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.681608 · Cited by 40

Learning to rank only using training data from related domain

Gao, W. G., Cai, P., Wong, K., & Zhou, A. (2010) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1835449.1835478 · Cited by 41

A Domain Adaptation Regularization for Denoising Autoencoders

Clinchant, S., Csurka, G., & Chidlovskii, B. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2005 · Cited by 40

Feature Adaptation and Augmentation for Cross-Scene Hyperspectral Image Classification

Shen, J., Cao, X., Li, Y., & Xu, D. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2799628 · Cited by 39

Collegial activity learning between heterogeneous sensors

Feuz, K., & Cook, D. J. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1043-3 · Cited by 39

A Survey on Sentiment Analysis

Routray, P., Swain, C. K., & Mishra, S. P. (2013) · International Journal of Computer Applications · doi:10.5120/13280-0527 · Cited by 39

Trada

Chen, K., Rong-qing, L., Wong, C. K., Sun, G., Heck, L. P., & Tseng, B. L. (2008) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1458082.1458233 · Cited by 41

Break-It-Fix-It: Unsupervised Learning for Program RepairPreprint

Yasunaga, M., & Liang, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06600 · Cited by 48

Set-Based Boosting for Instance-Level Transfer

Eaton, E., & desJardins, M. (2009) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2009.97 · Cited by 38

Semi-supervised domain adaptation via Fredholm integral based kernel methods

Wang, W., Wang, H., Zhang, Z., Zhang, C., & Gao, Y. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.035 · Cited by 36

Unsupervised and Semi-Supervised Robust Spherical Space Domain Adaptation

Gu, X., Sun, J. Z., & Xu, Z. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3158637 · Cited by 37

Cross-Domain Facial Expression Recognition Using Supervised Kernel Mean Matching

Miao, Y.-Q., Araujo, R., & Kamel, M. S. (2012) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2012.178 · Cited by 35

Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation

Ziser, Y., & Reichart, R. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1591 · Cited by 36

Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

(2017) · doi:10.18653/v1/w17-52 · Cited by 36

Robust Covariate Shift Regression

Chen, X., Monfort, M., Liu, A., & Ziebart, B. D. (2016) · International Conference on Artificial Intelligence and Statistics · Cited by 37

Stacked Robust Adaptively Regularized Auto-Regressions for Domain Adaptation

Jiang, W., Gao, H., Lu, W., Liu, W., Chung, F.-L., & Huang, H. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2837085 · Cited by 34

A Survey on Deep Learning in COVID-19 Diagnosis

Han, X., Hu, Z., Wang‎, S., & Zhang, Y. (2022) · Journal of Imaging · doi:10.3390/jimaging9010001 · Cited by 35

A two-stage framework for cross-domain sentiment classification

Wu, Q., & Tan, S. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.04.240 · Cited by 35

Transfer learning with one-class data

Chen, J., & Liu, X. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.07.017 · Cited by 34

Model-Driven Domain Adaptation on Product Manifolds for Unconstrained Face Recognition

Ho, H. T., & Gopalan, R. (2014) · International Journal of Computer Vision · doi:10.1007/s11263-014-0720-x · Cited by 33

Classification Trees for Aquatic Vegetation Community Prediction From Imaging Spectroscopy

Hestir, E. L., Greenberg, J. A., & Ustin, S. L. (2012) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2012.2200878 · Cited by 34

Semi‐ and fully supervised quantification techniques to improve population estimates from machine classifiers

Orenstein, E. C., Kenitz, K. M., Roberts, P. L. D., Franks, P. J. S., Jaffe, J. S., & Barton, A. D. (2020) · Limnology and Oceanography Methods · doi:10.1002/lom3.10399 · Cited by 39

Exploring Representation-Learning Approaches to Domain Adaptation

Huang, F., & Yates, A. (2010) · Cited by 34

Domain adaptation for speech emotion recognition by sharing priors between related source and target classes

Mao, Q., Xue, W., Rao, Q., Zhang, F., & Zhan, Y. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472149 · Cited by 34

Deep Learning and Ensemble Methods for Domain Adaptation

Nozza, D., Fersini, E., & Messina, E. (2016) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2016.0037 · Cited by 35

Open Challenges in Synthetic Speech Detection

Cuccovillo, L., Papastergiopoulos, C., Vafeiadis, A., Yaroshchuk, A., Aichroth, P., Votis, K., & Tzovaras, D. (2022) · 2022 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs55849.2022.9975433 · Cited by 33

Robust and high-order correlation alignment for unsupervised domain adaptation

Cheng, Z., Chen, C., Chen, Z., Fang, K., & Jin, X. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05465-7 · Cited by 35

Robustness of AI-based prognostic and systems health management

Khan, S., Tsutsumi, S., Yairi, T., & Nakasuka, S. (2021) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2021.04.001 · Cited by 33

Cross-domain sentiment classification via topical correspondence transfer

Zhou, G., Zhou, Y., Guo, X., Tu, X., & He, T. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.12.006 · Cited by 32

Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer

Samat, A., Gamba, P. E., Abuduwaili, J., Liu, S., & Miao, Z. (2016) · Remote Sensing · doi:10.3390/rs8030234 · Cited by 32

Question Independent Grading using Machine Learning

Singh, G., Srikant, S., & Aggarwal, V. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939696 · Cited by 32

Data-driven fault diagnosis in SOFC-based power plants under off-design operating conditions

Costamagna, P., Giorgi, A. D., Moser, G., Pellaco, L., & Trucco, A. (2019) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2019.09.128 · Cited by 32

Cross lingual speech emotion recognition via triple attentive asymmetric convolutional neural network

Ocquaye, E. N. N., Mao, Q., Xue, Y., & Song, H. (2020) · International Journal of Intelligent Systems · doi:10.1002/int.22291 · Cited by 30

Multi-Reference Neural TTS Stylization with Adversarial Cycle Consistency

Whitehill, M., Ma, S., McDuff, D., & Song, Y. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2985 · Cited by 32

Exploring subdomain variation in biomedical language

Lippincott, T., Séaghdha, D. Ó., & Korhonen, A. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-212 · Cited by 38

Cross-domain representation-learning framework with combination of class-separate and domain-merge objectives

Tu, W., & Sun, S. (2012) · doi:10.1145/2351333.2351336 · Cited by 31

Robust Learning with the Hilbert-Schmidt Independence CriterionPreprint

Greenfeld, D., & Shalit, U. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.00270 · Cited by 36

BabelDomains: Large-Scale Domain Labeling of Lexical Resources

Camacho-Collados, J., & Roberto Navigli, A. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2036 · Cited by 31

Considerations in Assuring Safety of Increasingly Autonomous Systems

Alves, E. E., Bhatt, D., Hall, B., Driscoll, K. R., Murugesan, A., & Rushby, J. (2018) · NASA Technical Reports Server (NASA) · Cited by 30

Improving the Stability of Intrusion Detection With Causal Deep Learning

Zeng, Z., Peng, W., & Zeng, D. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3193099 · Cited by 31

Compositional Dictionaries for Domain Adaptive Face Recognition

Qiu, Q., & Chellappa, R. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2479456 · Cited by 30

Pocket Data Mining

Gaber, M. M., Stahl, F. T., & Gomes, J. B. (2013) · Studies in big data · doi:10.1007/978-3-319-02711-1 · Cited by 30

Joshua 4.0: Packing, PRO, and Paraphrases

Ganitkevitch, J., Cao, Y., Weese, J., Post, M., & Callison-Burch, C. (2013) · Cited by 30

A Comparative Study of Cross-Lingual Sentiment Classification

Wan, X. (2012) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2012.54 · Cited by 30

Comparable Study of Event Extraction in Newswire and Biomedical Domains

Miwa, M., Thompson, P. M., Korkontzelos, I., & Ananiadou, S. (2014) · Research Explorer (The University of Manchester) · Cited by 29

Predicting proficiency levels in learner writings by transferring a linguistic complexity model from expert-written coursebooks

Pilán, I., Volodina, E., & Zesch, T. (2016) · DuEPublico (University of Duisburg-Essen) · doi:10.17185/duepublico/72093 · Cited by 29

PADA: A Prompt-based Autoregressive Approach for Adaptation to Unseen Domains.Preprint

Ben‐David, E., Oved, N., & Reichart, R. (2021) · arXiv (Cornell University) · Cited by 30

Regularized Transfer Boosting for Face Detection Across Spectrum

Zhang, Z., Yi, D., Lei, Z., & Li, S. (2011) · IEEE Signal Processing Letters · doi:10.1109/lsp.2011.2171949 · Cited by 28

Noise Reference Signal–Based Denoising Method for EDA Collected by Multimodal Biosensor Wearable in the Field

Lee, G., Choi, B., Jebelli, H., Ahn, C. R., & Lee, S. H. (2020) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000927 · Cited by 28

Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach

Sarwar, S. M., & Murdock, V. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19340 · Cited by 31

LM-Critic: Language Models for Unsupervised Grammatical Error Correction

Yasunaga, M., Leskovec, J., & Liang, P. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.611 · Cited by 30

Unknown Examples & Machine Learning Model GeneralizationPreprint

Chung, Y., Haas, P. J., Upfal, E., & Kraska, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.08294 · Cited by 43

On robustness and domain adaptation using SVD for word sense disambiguation

Agirre, E., & de Lacalle, O. L. (2008) · International Conference on Computational Linguistics · doi:10.3115/1599081.1599084 · Cited by 27

Linguistic steganography on Twitter: hierarchical language modeling with manual interaction

Wilson, A., Blunsom, P., & Ker, A. D. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2039213 · Cited by 27

Multi-source deep transfer learning for cross-sensor biometrics

Kandaswamy, C., Monteiro, J. C., Silva, L. M., & Cardoso, J. S. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2325-5 · Cited by 27

Quantification in-the-wild: data-sets and baselinesPreprint

Beijbom, O., Hoffman, J., Yao, E., Darrell, T., Ramirez, A. R., González‐Rivero, M., & Hoegh‐Guldberg, O. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.04811 · Cited by 27

Cross-domain sentiment classification-feature divergence, polarity divergence or both?

Zhang, Y., Hu, X., Li, P., Li, L., & Wu, X. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.07.006 · Cited by 26

Adversarial balancing-based representation learning for causal effect inference with observational data

Du, X., Сун, Л., Duivesteijn, W., Nikolaev, A. G., & Pechenizkiy, M. (2021) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-021-00759-3 · Cited by 40

Pre-train or Annotate? Domain Adaptation with a Constrained Budget

Bai, F., Ritter, A., & Xu, W. H. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.409 · Cited by 27

Supervised domain adaption for WSD

Agirre, E., & de Lacalle, O. L. (2009) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/1609067.1609071 · Cited by 26

Heterogeneous transfer learning for activity recognition using heuristic search techniques

Feuz, K., & Cook, D. J. (2014) · International Journal of Pervasive Computing and Communications · doi:10.1108/ijpcc-03-2014-0020 · Cited by 27

Knowledge transfer for spectral clustering

Jiang, W., Liu, W., & Chung, F.-L. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.04.018 · Cited by 26

SemEval-2010 task 17

Agirre, E., de Lacalle, O. L., Fellbaum, C., Marchetti, A., Toral, A., & Vossen, P. T. J. M. (2009) · Workshop on Semantic Evaluations: Recent Achievements and Future Directions (SEW) · doi:10.3115/1621969.1621991 · Cited by 26

Regularized adaptation: theory, algorithms and applications

Bilmes, J., & Xiao, L. (2007) · Cited by 26

Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages

Imran, M., Mitra, P., & Srivastava, J. (2016) · arXiv · doi:10.48550/arxiv.1602.05388 · Cited by 26

Mixing Multiple Translation Models in Statistical Machine Translation

Razmara, M., Foster, G. F., Sankaran, B., & Sarkar, A. (2012) · Cited by 25

The l2,1-Norm Stacked Robust Autoencoders for Domain Adaptation

Jiang, W., Gao, H., Chung, F.-L., & Huang, H. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10274 · Cited by 25

DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation

Calderon, N., Ben-David, E., Feder, A., & Reichart, R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.533 · Cited by 39

Which Clustering Do You Want? Inducing Your Ideal Clustering with Minimal Feedback

Dasgupta, S., & Ng, V. (2010) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3003 · Cited by 25

Relative deviation learning bounds and generalization with unbounded loss functions

Cortes, C., Greenberg, S., & Mohri, M. (2019) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-018-9613-y · Cited by 25

Model Transport: Towards Scalable Transfer Learning on Manifolds

Freifeld, O., Hauberg, S., & Black, M. J. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.179 · Cited by 25

Bagging based ensemble transfer learning

Liu, X., Wang, G., Cai, Z., & Zhang, H. (2015) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-015-0296-5 · Cited by 25

Semantic Abstraction for generalization of tweet classification: An evaluation of incident-related tweets

Schulz, A., Christian Guckelsberger, B., & Janssen, F. W. (2016) · Semantic Web · doi:10.3233/sw-150188 · Cited by 25

Scene-Adaptive Vehicle Detection Algorithm Based on a Composite Deep Structure

Cai, Y.-F., Wang, H., Zheng, Z., & Sun, X.-Q. (2017) · IEEE Access · doi:10.1109/access.2017.2756081 · Cited by 24

Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN

Marrese-Taylor, E., Balazs, J., & Matsuo, Y. (2017) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w17-5213 · Cited by 25

Domain Adaptations for Computer Vision ApplicationsPreprint

Beijbom, O. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1211.4860 · Cited by 29

Stable Adversarial Learning under Distributional Shifts

Liu, J., Shen, Z., Cui, P., Zhou, L., Kuang, K., Li, B., & Lin, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17050 · Cited by 24

From micro- to nano- and time-resolved x-ray computed tomography: Bio-based applications, synchrotron capabilities, and data-driven processing

Claro, P. I. C., Borges, E. P. B. S., Schleder, G. R., Archilha, N. L., Pinto, A. D. S., Carvalho, M. D. O., Driemeier, C., Fazzio, A., & Gouveia, R. F. (2023) · Applied Physics Reviews · doi:10.1063/5.0129324 · Cited by 25

SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation

Michele, B., Boulch, A., Puy, G., Vu, T.-H., Marlet, R., & Courty, N. (2024) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00134 · Cited by 24

Information-Theoretic Multi-view Domain Adaptation: A Theoretical and Empirical Study

Yang, P., & Gao, W. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4190 · Cited by 24

On Deep Learning in Cross-Domain Sentiment Classification

Domeniconi, G., Moro, G., Pagliarani, A., & Pasolini, R. (2017) · International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management · doi:10.5220/0006488100500060 · Cited by 24

Hypothesis transfer learning based on structural model similarity

Fernandes, K., & Cardoso, J. S. (2017) · Neural Computing and Applications · doi:10.1007/s00521-017-3281-4 · Cited by 24

Reuse and Adaptation for Entity Resolution through Transfer LearningPreprint

Thirumuruganathan, S., Parambath, S. A. P., Ouzzani, M., Tang, N., & Joty, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.11084 · Cited by 39

Application of Transfer Learning in Continuous Time Series for Anomaly Detection in Commercial Aircraft Flight Data

Xiong, P., Zhu, Y., Sun, Z., Cao, Z., Wang, M., Zheng, Y., Hou, J., Huang, T., & Que, Z. (2018) · IEEE International Conference on Smart Cloud (SmartCloud) · doi:10.1109/smartcloud.2018.00011 · Cited by 24

Domain Adaptation--Can Quantity compensate for Quality?.

Ben-David, S., Shalev‐Shwartz, S., & Urner, R. (2012) · Cited by 9

A survey on domain adaptation theory: learning bounds and theoretical guaranteesPreprint

Redko, I., Morvant, E., Habrard, A., Sebban, M., & Bennani, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.11829 · Cited by 154

A two-stage approach to domain adaptation for statistical classifiers

Jiang, J., & Zhai, C. (2007) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1321440.1321498 · Cited by 119

Domain Adaptation: A Small Sample Statistical Approach

Salakhutdinov, R., Kakade, S. M., & Foster, D. P. (2012) · International Conference on Artificial Intelligence and Statistics

Advances in Domain Adaptation Theory

Redko, I., Morvant, E., Habrard, A., Sebban, M., & Bennani, Y. (2019) · HAL · Cited by 65