393 citations · 757 across the 5 of their papers we have counts for
10 papers
Skillful Precipitation Nowcasting using Deep Generative Models of Radar
Suman Ravuri, Karel Lenc, Matthew Willson +17
Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socio-economic needs of many sectors reliant on weather-de…
Perceiver: General Perception with Iterative Attention
Andrew Jaegle, Felix Gimeno, Andrew Brock +3
Biological systems perceive the world by simultaneously processing high-dimensional inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The perceptio…
High-Performance Large-Scale Image Recognition Without Normalization
Andrew Brock, Soham De, Samuel L. Smith +1
Batch normalization is a key component of most image classification models, but it has many undesirable properties stemming from its dependence on the batch size and interactions b…
Characterizing signal propagation to close the performance gap in unnormalized ResNets
Andrew Brock, Soham De, Samuel L. Smith
Batch Normalization is a key component in almost all state-of-the-art image classifiers, but it also introduces practical challenges: it breaks the independence between training ex…
Training Generative Adversarial Networks by Solving Ordinary Differential Equations
Chongli Qin, Yan Wu, Jost Tobias Springenberg +4
The instability of Generative Adversarial Network (GAN) training has frequently been attributed to gradient descent. Consequently, recent methods have aimed to tailor the models an…
BYOL works even without batch statistics
Pierre H. Richemond, Jean-Bastien Grill, Florent Altché +8
Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach for image representation. From an augmented view of an image, BYOL trains an online network to predict a tar…