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OpenAI researchers present a Variational Lossy Autoencoder (VLAE) that combines VAEs with neural autoregressive models (RNN, MADE, PixelRNN/CNN) to learn controllable global representations, achieving state-of-the-art results on MNIST, OMNIGLOT, and Caltech-101 Silhouettes density estimation tasks.
OpenAI publishes an overview of generative models as an approach to developing machine understanding of the world, explaining how these models work by learning to generate data similar to their training sets and their potential applications across various domains.
OpenAI announces the arrival of several prominent machine learning researchers and engineers, including Ian Goodfellow, Alec Radford, and Yura Burda, joining the team in recent months. The announcement highlights the diverse expertise and notable contributions of new hires spanning generative modeling, reinforcement learning, and deep learning.