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Found 11 papers, 0 papers with code
DisC-VC: Disentangled and F0-Controllable Neural Voice Conversion
In this paper, we propose a new variational-autoencoder-based voice conversion model accompanied by an auxiliary network, which ensures that the conversion result correctly reflects the specified F0/timbre information.
AutoLL: Automatic Linear Layout of Graphs based on Deep Neural Network
However, it is limited to a two-mode reordering (i. e., the rows and columns are reordered separately) and it cannot be applied in the one-mode setting (i. e., the same node order is used for reordering both rows and columns), owing to the characteristics of its model architecture.
Deep Two-Way Matrix Reordering for Relational Data Analysis
This denoised mean matrix can be used to visualize the global structure of the reordered observed matrix.
A Goodness-of-fit Test on the Number of Biclusters in a Relational Data Matrix
Biclustering is a method for detecting homogeneous submatrices in a given observed matrix, and it is an effective tool for relational data analysis.
X-DC: Explainable Deep Clustering based on Learnable Spectrogram Templates
Particularly, it has been shown that a monaural speech separation task can be successfully solved with a DNN-based method called deep clustering (DC), which uses a DNN to describe the process of assigning a continuous vector to each time-frequency (TF) bin and measure how likely each pair of TF bins is to be dominated by the same speaker.
Selective Inference for Latent Block Models
In this case, it becomes crucial to consider the selective bias in the block structure, that is, the block structure is selected from all the possible cluster memberships based on some criterion by the clustering algorithm.
Goodness-of-fit Test for Latent Block Models
Latent block models are used for probabilistic biclustering, which is shown to be an effective method for analyzing various relational data sets.
Interpreting Layered Neural Networks via Hierarchical Modular Representation
Interpreting the prediction mechanism of complex models is currently one of the most important tasks in the machine learning field, especially with layered neural networks, which have achieved high predictive performance with various practical data sets.
Knowledge Discovery from Layered Neural Networks based on Non-negative Task Decomposition
Interpretability has become an important issue in the machine learning field, along with the success of layered neural networks in various practical tasks.
Understanding Community Structure in Layered Neural Networks
We show experimentally that our proposed method can reveal the role of each part of a layered neural network by applying the neural networks to three types of data sets, extracting communities from the trained network, and applying the proposed method to the community structure.
Modular Representation of Layered Neural Networks
And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network.
