Learning Discriminative Fisher Kernels

By L. V. D. van der Maaten. 2011

89 citations recorded by the source corpus.

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Citations in the corpus

Pre-training Graph Neural Networks with Kernels (2018, neutral): “Although there are examples of kernels for structures that can be designed on the basis of a training set [van der Maaten, 2011; Aiolli et al. , 2009; Bacciu et al. , 2018], most of the more efﬁcient and effective graph kernels are based on predeﬁned structural features, i.e, features deﬁnition is…”
Task2Vec: Task Embedding for Meta-Learning (2019, background): “[35] learns a metric on the Fisher kernel representation similar to our metric learning approach.”
On Filter Size in Graph Convolutional Networks (2018, background): “Although there are examples of kernels for structures that can be designed on the basis of a training set [4]–[6], most of the more efficient and effective graph kernels are based on predefined structural features, i.”
Score Function Features for Discriminative Learning: Matrix and Tensor Framework (2014, background): “Instead there have been attempts to construct discriminative features from generative models using labeled samples (Maaten, 2011; Mairal et al., 2009; Wang et al., 2013).”
Neural Networks for Sequential Data: a Pre-training Approach based on Hidden Markov Models (2015, methodology): “A technique that tries to correct this problem has been proposed in [26].”
Multivariate LSTM-FCNs for Time Series Classification (2018, background): “[20, 21] Another common approach for multivariate time series classification is by applying dimensional reduction techniques or by concatenating all dimensions of a multivariate time series into a univariate time series.”