201 citations · 315 across the 9 of their papers we have counts for
11 papers
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras +2
Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However,…
Do We Still Need Clinical Language Models?
Eric Lehman, Evan Hernandez, Diwakar Mahajan +7
Although recent advances in scaling large language models (LLMs) have resulted in improvements on many NLP tasks, it remains unclear whether these models trained primarily with gen…
Procedural Image Programs for Representation Learning
Manel Baradad, Chun-Fu Chen, Jonas Wulff +4
Learning image representations using synthetic data allows training neural networks without some of the concerns associated with real images, such as privacy and bias. Existing wor…
Learning to See by Looking at Noise
Manel Baradad, Jonas Wulff, Tongzhou Wang +2
Current vision systems are trained on huge datasets, and these datasets come with costs: curation is expensive, they inherit human biases, and there are concerns over privacy and u…
Using latent space regression to analyze and leverage compositionality in GANs
Lucy Chai, Jonas Wulff, Phillip Isola
In recent years, Generative Adversarial Networks have become ubiquitous in both research and public perception, but how GANs convert an unstructured latent code to a high quality o…
Improving Inversion and Generation Diversity in StyleGAN using a Gaussianized Latent Space
Jonas Wulff, Antonio Torralba
Modern Generative Adversarial Networks are capable of creating artificial, photorealistic images from latent vectors living in a low-dimensional learned latent space. It has been s…