2.3k citations · 2.5k across the 6 of their papers we have counts for
6 papers
Hierarchical Text-Conditional Image Generation with CLIP Latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol +2
Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation…
Asymmetric self-play for automatic goal discovery in robotic manipulation
OpenAI OpenAI, Matthias Plappert, Raul Sampedro +13
We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, including tasks with previously unseen goals and objects. We rely on asymmetric self-play…
The equivalence between Stein variational gradient descent and black-box variational inference
Casey Chu, Kentaro Minami, Kenji Fukumizu
We formalize an equivalence between two popular methods for Bayesian inference: Stein variational gradient descent (SVGD) and black-box variational inference (BBVI). In particular,…
Smoothness and Stability in GANs
Casey Chu, Kentaro Minami, Kenji Fukumizu
Generative adversarial networks, or GANs, commonly display unstable behavior during training. In this work, we develop a principled theoretical framework for understanding the stab…
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning
Casey Chu, Jose Blanchet, Peter Glynn
This paper provides a unifying view of a wide range of problems of interest in machine learning by framing them as the minimization of functionals defined on the space of probabili…
CycleGAN, a Master of Steganography
Casey Chu, Andrey Zhmoginov, Mark Sandler
CycleGAN (Zhu et al. 2017) is one recent successful approach to learn a transformation between two image distributions. In a series of experiments, we demonstrate an intriguing pro…