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Deep Generative Models and Downstream Applications
José Miguel Hernández-Lobato · Yingzhen Li · Yichuan Zhang · Cheng Zhang · Austin Tripp · Weiwei Pan · Oren Rippel

Tue Dec 14 06:00 AM -- 03:00 PM (PST) @ None
Event URL: https://dgms-and-applications.github.io/2021/ »

Deep generative models (DGMs) have become an important research branch in deep learning, including a broad family of methods such as variational autoencoders, generative adversarial networks, normalizing flows, energy based models and autoregressive models. Many of these methods have been shown to achieve state-of-the-art results in the generation of synthetic data of different types such as text, speech, images, music, molecules, etc. However, besides just generating synthetic data, DGMs are of particular relevance in many practical downstream applications. A few examples are imputation and acquisition of missing data, anomaly detection, data denoising, compressed sensing, data compression, image super-resolution, molecule optimization, interpretation of machine learning methods, identifying causal structures in data, generation of molecular structures, etc. However, at present, there seems to be a disconnection between researchers working on new DGM-based methods and researchers applying such methods to practical problems (like the ones mentioned above). This workshop aims to fill in this gap by bringing the two aforementioned communities together.

Author Information

José Miguel Hernández-Lobato (University of Cambridge)
Yingzhen Li (Imperial College London)
Yichuan Zhang (Boltzbit Limited)
Cheng Zhang (Microsoft Research, Cambridge, UK)

Cheng Zhang is a principal researcher at Microsoft Research Cambridge, UK. She leads the Data Efficient Decision Making (Project Azua) team in Microsoft. Before joining Microsoft, she was with the statistical machine learning group of Disney Research Pittsburgh, located at Carnegie Mellon University. She received her Ph.D. from the KTH Royal Institute of Technology. She is interested in advancing machine learning methods, including variational inference, deep generative models, and sequential decision-making under uncertainty; and adapting machine learning to social impactful applications such as education and healthcare. She co-organized the Symposium on Advances in Approximate Bayesian Inference from 2017 to 2019.

Austin Tripp (University of Cambridge)
Weiwei Pan (Harvard University)
Oren Rippel (WaveOne, Inc.)

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