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Modern neural models trained on textual data rely on pre-trained representations that emerge without direct supervision.
Bias in bios: A case study of semantic representation bias in a high-stakes setting
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Karl Pearson. 1901 · 1901
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Rank correlation and tests of significance involving no assumption of normality
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Theory of Games and Economic Behavior
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Sur un théoreme fondamental de la théorie des jeux
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Estimation of principal components and related models by iterative least squares
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Nonlinear iterative partial least squares (NIPALS) modelling: Some current developments
Herman Wold. 1973 · 1973
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Subspace clustering for high dimensional data: a review
Lance Parsons, Ehtesham Haque, and Huan Liu. 2004 · 2004
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Minimax theorems revisited
Hoang Tuy. 2004 · 2004
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Mathematical Methods for Physicists: A Comprehensive Guide
George B. Arfken, Hans J. Weber, and Frank E. Harris. 2011 · 2011
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Scikit-learn: Machine learning in Python
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Matrix Analysis
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Fantope projection and selection: A near-optimal convex relaxation of sparse PCA
Vincent Q. Vu, Juhee Cho, Jing Lei, and Karl Rohe. 2013 · 2013
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Convex Optimization
Stephen P. Boyd and Lieven Vandenberghe. 2014 · 2014
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Censoring representations with an adversary
Harrison Edwards and Amos Storkey. 2015 · 2015
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Yaroslav Ganin and Victor Lempitsky. 2015 · 2015
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Felix Hill, Roi Reichart, and Anna Korhonen. 2015 · 2015
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From facial parts responses to face detection: A deep learning approach
Shuo Yang, Ping Luo, Chen-Change Loy, and Xiaoou Tang. 2015 · 2015
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Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, and Adam T. Kalai. 2016 · 2016
Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them
Hila Gonen and Yoav Goldberg. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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On the global optima of kernelized adversarial representation learning
Bashir Sadeghi, Runyi Yu, and Vishnu Boddeti. 2019 · 2019
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Controlling the imprint of passivization and negation in contextualized representations
Hande Celikkanat, Sami Virpioja, Jörg Tiedemann, and Marianna Apidianaki. 2020 · 2020
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It’s not Greek to mBERT: Inducing word-level translations from multilingual BERT
Hila Gonen, Shauli Ravfogel, Yanai Elazar, and Yoav Goldberg. 2020 · 2020
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Semantics derived automatically from language corpora necessarily contain human biases
Aylin Caliskan Islam, Joanna J. Bryson, and Arvind Narayanan. 2016 · 2016
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Context2vec: Learning generic context embedding with bidirectional LSTM
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Controllable invariance through adversarial feature learning
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Adversarial removal of demographic attributes from text data
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Spectral theorem
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Exploring the linear subspace hypothesis in gender bias mitigation
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Transformers: State-of-the-art natural language processing
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The complexity of constrained min-max optimization
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The low-dimensional linear geometry of contextualized word representations
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Effects of pre- and post-processing on type-based embeddings in lexical semantic change detection
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Counterfactual interventions reveal the causal effect of relative clause representations on agreement prediction
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