Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis
暂无分享,去创建一个
Shrey Desai | Junyi Jessy Li | Alex Rosenfeld | Barea Sinno | Barea M. Sinno | Shrey Desai | Alex Rosenfeld
[1] 悠太 菊池,et al. 大規模要約資源としてのNew York Times Annotated Corpus , 2015 .
[2] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[3] Diyi Yang,et al. Hierarchical Attention Networks for Document Classification , 2016, NAACL.
[4] Yoshua Bengio,et al. Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.
[5] Regina Barzilay,et al. Aspect-augmented Adversarial Networks for Domain Adaptation , 2017, TACL.
[6] Jacob Cohen. A Coefficient of Agreement for Nominal Scales , 1960 .
[7] Ralph Grishman,et al. Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network , 2017, IJCNLP.
[8] Michael J. Paul,et al. Examining Temporality in Document Classification , 2018, ACL.
[9] Timo Aila,et al. Temporal Ensembling for Semi-Supervised Learning , 2016, ICLR.
[10] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[11] Vladlen Koltun,et al. Trellis Networks for Sequence Modeling , 2018, ICLR.
[12] Geoffrey French,et al. Self-ensembling for visual domain adaptation , 2017, ICLR.
[13] Noah A. Smith,et al. Etch-a-Sketching: Evaluating the Post-Primary Rhetorical Moderation Hypothesis , 2018, American Politics Research.
[14] Junyi Jessy Li,et al. Domain Agnostic Real-Valued Specificity Prediction , 2018, AAAI.
[15] Kilian Q. Weinberger,et al. Marginalized Denoising Autoencoders for Domain Adaptation , 2012, ICML.
[16] D. Rucinski. The Nature and Origins of Mass Opinion. , 1994 .
[17] Alex Graves,et al. Neural Machine Translation in Linear Time , 2016, ArXiv.
[18] Yonatan Belinkov,et al. Identifying and Controlling Important Neurons in Neural Machine Translation , 2018, ICLR.
[19] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[20] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[21] M. Baum,et al. The Relationships Between Mass Media, Public Opinion, and Foreign Policy: Toward a Theoretical Synthesis , 2008 .
[22] Adam J. Berinsky,et al. The Illusion of Public Opinion: Fact and Artifact in American Public Opinion Polls , 2005, Perspectives on Politics.
[23] P. Schmidt,et al. Measurement Equivalence in Cross-National Research , 2014 .
[24] Michael J. Ensley,et al. Policy and the structure of roll call voting in the US house , 2020, Journal of Public Policy.
[25] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] Yoon Kim,et al. Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.
[27] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[28] A. Gelman,et al. Partisans without Constraint: Political Polarization and Trends in American Public Opinion. , 2008, AJS; American journal of sociology.
[29] Jason Weston,et al. Curriculum learning , 2009, ICML '09.
[30] Joe Bob Hester. Setting the Agenda: The Mass Media and Public Opinion , 2005 .
[31] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[32] Zhiting Hu,et al. Improved Variational Autoencoders for Text Modeling using Dilated Convolutions , 2017, ICML.
[33] Léon Bottou,et al. Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.
[34] Robert E. Goodin,et al. The Oxford Handbook of Political Science , 2011 .
[35] Harri Valpola,et al. Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.
[36] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[37] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[38] Vladlen Koltun,et al. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling , 2018, ArXiv.
[39] Andrew Gordon Wilson,et al. There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average , 2018, ICLR.
[40] P. Converse,et al. The American voter , 1960 .
[41] Claire Cardie,et al. Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification , 2016, TACL.
[42] Angus Campbell,et al. The American voter , 1960 .