Recurrent neural networks (RNNs) in combination with a pooling op-erator and the neighbourhood components analysis (NCA) objective func-tion are able to detect the characterizing dynamics of sequences and embed them into a fixed-length vector space of arbitrary dimensionality. Subse-quently, the resulting features are meaningful and can be used for visu-alization or nearest neighbour classification in linear time. This kind of metric learning for sequential data enables the use of algorithms tailored towards fixed length vector spaces such as Rn. 1
Do you want your neural net algorithm to learn sequences? Do not lim-it yourself to conventional gra...
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the conn...
Recent advancements in recurrent neural network (RNN) research have demonstrated the superiority of ...
Storing short descriptors of sequential data has several benefits. First, they typically require muc...
International audienceSequence metric learning is becoming a widely adopted approach for various app...
Sequence processing involves several tasks such as clustering, classification, prediction, and trans...
In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in th...
Learning a distance metric provides solutions to many problems where the data exists in a high dimen...
International audienceRecurrent neural networks are powerful models for sequential data, ableto repr...
Many real-world sequence learning tasks re-quire the prediction of sequences of labels from noisy, u...
International audienceThis work proposes a temporal and frequential metric learning framework for a ...
The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet...
Abstract. Metric learning methods have been shown to perform well on different learning tasks. Many ...
Abstract—Recurrent networks can generate spatio-temporal neural sequences of very large cycles, havi...
This thesis studies the introduction of a priori structure into the design of learning systems based...
Do you want your neural net algorithm to learn sequences? Do not lim-it yourself to conventional gra...
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the conn...
Recent advancements in recurrent neural network (RNN) research have demonstrated the superiority of ...
Storing short descriptors of sequential data has several benefits. First, they typically require muc...
International audienceSequence metric learning is becoming a widely adopted approach for various app...
Sequence processing involves several tasks such as clustering, classification, prediction, and trans...
In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in th...
Learning a distance metric provides solutions to many problems where the data exists in a high dimen...
International audienceRecurrent neural networks are powerful models for sequential data, ableto repr...
Many real-world sequence learning tasks re-quire the prediction of sequences of labels from noisy, u...
International audienceThis work proposes a temporal and frequential metric learning framework for a ...
The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet...
Abstract. Metric learning methods have been shown to perform well on different learning tasks. Many ...
Abstract—Recurrent networks can generate spatio-temporal neural sequences of very large cycles, havi...
This thesis studies the introduction of a priori structure into the design of learning systems based...
Do you want your neural net algorithm to learn sequences? Do not lim-it yourself to conventional gra...
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the conn...
Recent advancements in recurrent neural network (RNN) research have demonstrated the superiority of ...