Easy Trick to Debias GenAI Models: Quantile Convolution
All of the GenAI apps that I tested, including my own, have the same problem. They cannot easily generate data outside the observation range. As an example, let’s focus on the insurance dataset discussed in my new book. I use it to generate synthetic data with GAN (generative adversarial networks) and the NoGAN models discussed […]
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New Book: Understanding Deep Learning
By Simon Prince, computer science Professor at the University of Alberta. To be published by MIT Press, Dec 2023. The author shares the associated Jupyter notebooks on his website, here. Very popular, it got over 5,000 likes when the author announced the upcoming book on LinkedIn. I pre-ordered my copy. Summary An authoritative, accessible, and […]
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NoGAN: Ultrafast Data Synthesizer – My Talk at ODSC San Francisco
My talk at the ODSC Conference, San Francisco, October 2023. Includes Notebook demonstration, using our open-source Python libraries. View or download the PowerPoint presentation, here. I discuss NoGAN, an alternative to standard tabular data synthetization. It runs 1000x faster than GAN, consistently delivering better results according to the most sophisticated evaluation metric, implemented here for […]
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Quantum Derivatives, GenAI, and the Riemann Hypothesis
Have you ever encountered a function or cumulative probability distribution (CDF) that is nowhere differentiable, yet continuous everywhere? Some are featured in this article. For a CDF, it means that it does not have a probability density function (PDF), and for a standard function, it has no derivative. At least, not until now. The quantum […]
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