Semi-Supervised Learning (Adaptive Computation and Machine Learning)

By Olivier Chapelle, Bernhard Schölkopf, Alexander Zien. 2006

281 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

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In Representation & Self-Supervised Learning

Citations in the corpus

Snorkel: Rapid Training Data Creation with Weak Supervision (2017, background): “Other Forms of Supervision: Work on semi-supervised learning considers settings with some labeled data and a much larger set of unlabeled data, and then leverages various domain- and task-agnostic assumptions about smoothness, low-dimensional structure, or distance metrics to heuristically label the”
Natural Language Processing (Almost) from Scratch (2011, methodology): “Semi-supervised learning has been the object of much attention during the last few years (see Chapelle et al., 2006).”
Deep learning via semi-supervised embedding (2008, background): “…2005), label propagation (Zhu & Ghahramani, 2002) and LapSVM (Belkin et al., 2006), to name a few, make use of regularizers that are directly related to 1This is often referred to as the cluster assumption or the manifold assumption (Chapelle et al., 2006). unsupervised embedding algorithms.”
Tighter PAC-Bayes bounds through distribution-dependent priors (2013, background): “These ideas are typical in semi-supervised learning (e.g. Zhu et al., 2003; Belkin et al., 2006; Chapelle et al., 2006).”
Toward Supervised Anomaly Detection (2014, methodology): “In this article, we cast anomaly detection into the paradigm of semi-supervised learning (Chapelle, Schölkopf, & Zien, 2006).”
A unified architecture for natural language processing: deep neural networks with multitask learning (2008, methodology): “Previous Work in Semi-Supervised Learning For an overview of semi-supervised learning, see (Chapelle et al., 2006).”
Universum Prescription: Regularization Using Unlabeled Data (2015, methodology): “The most related ones are information regularization (Corduneanu & Jaakkola (2006)) and transduction learning (Chapelle et al. (2006a)) (Gammerman et al. (1998)).”
A Survey of Techniques for Event Detection in Twitter (2015, methodology): “Transfer learning methods are designed to extract useful knowledge from different but related domains (Chapelle et al. 2006; Zhu Updated on July 19, 2008).”
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness (2020, background): “The manifold assumption states that high dimensional data concentrates around a low-dimensional manifold (Chapelle et al., 2006).”
Structured Lexical Similarity via Convolution Kernels on Dependency Trees (2011, background): “…of generalized features or the deﬁnition of semantic similarities between them, e.g. as proposed in (Resnik, 1995; Jiang and Conrath, 1997; Schtze, 1998; Pedersen et al., 2004a; Bloehdorn and Mos-chitti, 2007b; Davis et al., 2007) or in semi-supervised settings, e.g. (Chapelle et al., 2006).”
Simple Spectral Graph Convolution (2021, background): “As K → ∞, H = ∑∞ k=1 T̃ k is the optimal diffused representation of the normalized Laplacian Regularization problem (Chapelle et al., 2006):”
Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins (2010, background): “, 2000), see (Chapelle et al., 2006) for a review.”
Deep learning from temporal coherence in video (2009, methodology): “Classical semi-supervised learning (Chapelle et al., 2006) and transduction (Vapnik, 1995) are machinelearning classi.cation techniques able to handle la- Appearing in Proceedings of the 26 th International Conference on Machine Learning, Montreal, Canada, 2009.”
Projected estimators for robust semi-supervised classification (2016, background): “6 of these are the semi-supervised learning benchmark datasets proposed by Chapelle et al. (2006), while the other 10 were retrieved from the UCI Machine Learning repository (Bache & Lichman, 2013).”