Machine Learning: Top 5 eBooks for Learning ML at Home
These eBooks are low-cost, online learning resources to help you get started with machine learning. While much has been written as to how machine learning...
The Human-AI Interface: Governance, Data, and Judgment
The human-AI interface is an essential factor in the value realization of AI. AI technology has the potential to unlock capabilities to gain operational...
Bridging the Gap Between Deep Learning and Human Intelligence
In this article, we cover the main points of a lecture by Yoshua Bengio, professor at the University of Montreal, founder and scientific director of...
Deepfakes present a new challenge for businesses, as cybercriminals use them to impersonate executives and request money transfers.
Deepfakes are realistic AI-generated videos that can be used to impersonate someone. They are becoming increasingly easy to create, and businesses are starting to see...
NNSVS: An Open-Source Toolkit for Neural Network-Based Singing Voice Synthesis Authors: Ryuichi Yamamoto, Reo Yoneyama, Tomoki Toda Abstract: This paper describes the design of NNSVS, an open-source software for neural network-based singing voice synthesis research. NNSVS is inspired by Sinsy, an open-source pioneer in singing voice synthesis research, and provides many additional features such as multi-stream models, autoregressive fundamental frequency models, and neural vocoders. Furthermore, NNSVS provides extensive documentation and numerous scripts to build complete singing voice synthesis systems. Experimental results demonstrate that our best system significantly outperforms our reproduction of Sinsy and other baseline systems. The toolkit is available at https://github.com/nnsvs/nnsvs. Systems: The following table summarizes the systems used in our experiments. All the models were trained on Namine Ritsu
Related articles NNSVS: A Neural Network-Based Singing Voice Synthesis Toolkit Submitted to ICASSP 2023 Read the complete article at: <a href=”https://r9y9.github.io/projects/nnsvs/””>r9y9.github.io...
AI, ML and DL: Key Differences
1. AI is a general term that refers to any system that can make automated decisions.
2. ML is a term that refers to any system...
The Evolution of AI
In the early 1960s, computer scientist Ed Feigenbaum became interested in “creating models of the thinking processes of scientists, especially the processes of empirical induction...
The Book of Minds
Author: Philip Ball In The Book of Minds, Philip Ball discusses the fallacy of the anthropocentric view of minds, which is to evaluate other agents...
CS 330: Deep Multi-Task and Meta Learning
Description: This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can...