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We propose TabTransformer, a novel deep tabular data modeling architecture for supervised and semi-supervised learning.
Visualizing and measuring the geometry of bert
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Morcos, A. S.; Yu, H.; Paganini, M.; and Tian, Y. 2019 · 1906
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TabNet: Attentive Interpretable Tabular Learning
Arik, S. O.; and Pfister, T. 2019 · 1908
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On the validity of self-attention as explanation in transformer models
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Semi-Supervised Learning with Normalizing Flows
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The use of the area under the ROC curve in the evaluation of machine learning algorithms
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Analyzing the effectiveness and applicability of co-training
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Learning from labeled and unlabeled data with label propagation
Zhu, X.; and Ghahramani, Z. 2002 · 2002
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Semi-supervised learning by entropy minimization
Grandvalet, Y.; and Bengio, Y. 2005 · 2005
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Entropy regularization
Grandvalet, Y.; and Bengio, Y. 2006 · 2006
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Visualizing data using t-SNE
Maaten, L. v. d.; and Hinton, G. 2008 · 2008
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Semi-supervised learning)
Chapelle, O.; Scholkopf, B.; and Zien, A. 2009 · 2009
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Semi-supervised learning. Adaptive Computation and Machine Learning
Chappelle, O.; Schölkopf, B.; and Zien, A. 2010 · 2010
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Extensions of recurrent neural network language model
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Lee, D.-H. 2013 · 2013
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word2vec parameter learning explained
Rong, X. 2014 · 2014
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Education level and jobs: Opportunities by state
Torpey, E.; and Watson, A. 2014 · 2014
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Artificial Neural Networks Applied to Taxi Destination Prediction
De Brébisson, A.; Simon, E.; Auvolat, A.; Vincent, P.; and Bengio, Y. 2015 · 2015
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Bidirectional LSTM-CRF models for sequence tagging
Huang, Z.; Xu, W.; and Yu, K. 2015 · 2015
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Xgboost: A scalable tree boosting system
Chen, T.; and Guestrin, C. 2016 · 2016
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Wide & deep learning for recommender systems
Cheng, H.-T.; Koc, L.; Harmsen, J.; Shaked, T.; Chandra, T.; Aradhye, H.; Anderson, G.; Corrado, G.; Chai, W.; Ispir, M.; et al. 2016 · 2016
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AdaNet: Adaptive Structural Learning of Artificial Neural Networks
Cortes, C.; Gonzalvo, X.; Kuznetsov, V.; Mohri, M.; and Yang, S. 2016 · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Sajjadi, M.; Javanmardi, M.; and Tasdizen, T. 2016 · 2016
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Deep Variational Information Bottleneck
Alemi, A. A.; Fischer, I.; Dillon, J. V.; and Murphy, K. 2017 · 2017
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UCI Machine Learning Repository
Analysis of the AutoML Challenge series 2015-2018
Guyon, I.; Sun-Hosoya, L.; Boullé, M.; Escalante, H. J.; Escalera, S.; Liu, Z.; Jajetic, D.; Ray, B.; Saeed, M.; Sebag, M.; Statnikov, A.; Tu, W.; and Viegas, E. 2019 · 2018
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Universal language model fine-tuning for text classification
Howard, J.; and Ruder, S. 2018 · 2018
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Porto Seguro’s Safe Driver Prediction
Jahrer, M. 2018 · 2018
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Realistic evaluation of deep semi-supervised learning algorithms
Oliver, A.; Odena, A.; Raffel, C. A.; Cubuk, E. D.; and Goodfellow, I. 2018 · 2018
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CatBoost: unbiased boosting with categorical features
Prokhorenkova, L.; Gusev, G.; Vorobev, A.; Dorogush, A. V.; and Gulin, A. 2018 · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
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Dua, D.; and Graff, C. 2017 · 2017
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Introduction to Pseudo-Labelling : A Semi-Supervised learning technique
Jain, S. 2017 · 2017
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The State of ML and Data Science 2017
Kaggle, Inc. 2017 · 2017
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LightGBM: A highly efficient gradient boosting decision tree
Ke, G.; Meng, Q.; Finley, T.; Wang, T.; Chen, W.; Ma, W.; Ye, Q.; and Liu, T.-Y. 2017 · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A.; and Gal, Y. 2017 · 2017
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Self-normalizing neural networks
Klambauer, G.; Unterthiner, T.; Mayr, A.; and Hochreiter, S. 2017 · 2017
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Decoupled Weight Decay Regularization
Loshchilov, I.; and Hutter, F. 2017 · 2017
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Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; and Chen, L.-C. 2018 · 2018
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Deep learning for computer vision: A brief review
Voulodimos, A.; Doulamis, N.; Doulamis, A.; and Protopapadakis, E. 2018 · 2018
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Yang, Y.; Morillo, I. G.; and Hospedales, T. M. 2018 · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
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Label propagation for deep semi-supervised learning
Iscen, A.; Tolias, G.; Avrithis, Y.; and Chum, O. 2019 · 2019
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TabNN: A Universal Neural Network Solution for Tabular Data
Ke, G.; Zhang, J.; Xu, Z.; Bian, J.; and Liu, T.-Y. 2019 · 2019
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Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
Oliver, A.; Odena, A.; Raffel, C.; Cubuk, E. D.; and Goodfellow, I. J. 2019 · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Kopf, A.; Yang, E.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; and Chintala, S. 2019 · 2019
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AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
Song, W.; Shi, C.; Xiao, Z.; Duan, Z.; Xu, Y.; Zhang, M.; and Tang, J. 2019 · 2019
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Graph Agreement Models for Semi-Supervised Learning
Stretcu, O.; Viswanathan, K.; Movshovitz-Attias, D.; Platanios, E.; Ravi, S.; and Tomkins, A. 2019 · 2019
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DeepEnFM: Deep neural networks with Encoder enhanced Factorization Machine URL https://openreview.net/forum?id=SJlyta4YPS
Sun, Q.; Cheng, Z.; Fu, Y.; Wang, W.; Jiang, Y.-G.; and Xue, X. 2019 · 2019
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ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Clark, K.; Luong, M.-T.; Le, Q. V.; and Manning, C. D. 2020 · 2020
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Interpretable Click-Through Rate Prediction through Hierarchical Attention
Li, Z.; Cheng, W.; Chen, Y.; Chen, H.; and Wang, W. 2020 · 2020
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