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Enhancing Zero-Shot Many to Many Voice Conversion with Self-Attention VAE
Variational auto-encoder (VAE) is an effective neural network architecture to disentangle a speech utterance into speaker identity and linguistic content latent embeddings, then generate an utterance for a target speaker from that of a source speaker.
Recurrence of Optimum for Training Weight and Activation Quantized Networks
Deep neural networks (DNNs) are quantized for efficient inference on resource-constrained platforms.
Learning Quantized Neural Nets by Coarse Gradient Method for Non-linear Classification
In this paper, we propose a class of STEs with certain monotonicity, and consider their applications to the training of a two-linear-layer network with quantized activation functions for non-linear multi-category classification.
Global Convergence and Geometric Characterization of Slow to Fast Weight Evolution in Neural Network Training for Classifying Linearly Non-Separable Data
In this paper, we study the dynamics of gradient descent in learning neural networks for classification problems.
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