Machine Learning and Knowledge Discovery in Databases (ECML PKDD)

By T. Snowsill, Ilias N. Flaounas, B. De, Nello Cristianini. 2010

79 citations recorded by the source corpus.

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Citations in the corpus

Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization (2017, background): “, adversarial test examples) in [6, 37] against SVMs and NNs, and subsequently in [27] against deep networks.”
Towards Adversarial Malware Detection (2018, methodology): “Evasion attacks (also known as adversarial examples) consist of minimizing the classifier’s score on the malicious class f (x ′) with respect to the input adversarial sample x ′ [8].”
Manifold Mixup: Better Representations by Interpolating Hidden States (2018, background): “At a high level, we can imagine systems in the brain which compute predictions from a stream of changing inputs, and pass these predictions onto other modules which return some kind of feedback signal (Lee et al., 2015; Scellier & Bengio, 2017; Whittington & Bogacz, 2017; Bartunov et al., 2018).”
Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations (2016, background): “Knowledge base completion via matrix or tensor factorization has received a lot of attention in the past few years (Bordes et al., 2011; Jenatton et al., 2012; Weston et al., 2013; Bordes et al., 2013; Socher et al., 2013; Garcı́a-Durán et al., 2014; Bordes et al., 2014; Lin et al., 2015b; Chang et ”
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges (2021, methodology): “…” values that work well under many conditions, by tuning ranges and HPC prior distributions based on extensive meta-data (Feurer, Klein, et al., 2015a; Gijsbers et al., 2021; Perrone et al., 2019; Pfisterer et al., 2021; Probst et al., 2019; Van Rijn & Hutter, 2018; Wistuba et al., 2015a, 2015b).”
Semi-supervised Learning with Ladder Networks (2015, unknown): “Embedding (Weston et al. , 2012) 16.86 5.73 1.5 Transductive SVM (from Weston et al. , 2012) 16.81 5.38 1.40* MTC (Rifai et al. , 2011b) 12.03 3.64 0.81 Pseudo-label (Lee, 2013) 10.49 3.46 AtlasRBF (Pitelis et al. , 2014) 8.10 ( ± 0.95) 3.68 ( ± 0.12) 1.31 DGN (Kingma et al. , 2014) 3.33 ( 0.14) 2.4”