K
Kilian Q. Weinberger
Researcher at Cornell University
Publications - 241
Citations - 71535
Kilian Q. Weinberger is an academic researcher from Cornell University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 76, co-authored 222 publications receiving 49707 citations. Previous affiliations of Kilian Q. Weinberger include University of Washington & Washington University in St. Louis.
Papers
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Journal ArticleDOI
Unsupervised Out-of-Distribution Detection with Diffusion Inpainting
TL;DR: LIFT, MAP, Detect (LMD) as discussed by the authors leverages diffusion models to detect out-of-distribution data by lifting an image off its original manifold by corrupting it, and mapping it towards the in-domain manifold with a diffusion model.
Proceedings ArticleDOI
Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs
Youya Xia,Josephine Monica,Wei-Lun Chao,Bharath Hariharan,Kilian Q. Weinberger,Mark E. Campbell +5 more
TL;DR: In this paper , a coarsely-aligned image-to-image translation model was proposed to supervise the image translation model in a self-driving car in adverse weather conditions.
Journal ArticleDOI
Does unsupervised grammar induction need pixels?
Boyi Li,Rodolfo Corona,Karttikeya Mangalam,Catherine S. Hsia Chen,Daniel Flaherty,S. Belongie,Kilian Q. Weinberger,Jitendra Malik,Trevor Darrell,Daniel Klein +9 more
TL;DR: This article investigated whether extralinguistic signals such as image pixels are crucial for inducing constituency grammars, and found that they persist in the presence of rich information from large language models.
Posted Content
Online Adaptation to Label Distribution Shift
TL;DR: In this article, the authors focus on adaptation to label distribution shift in the online setting, where the test-time label distribution is continually changing and the model must dynamically adapt to it without observing the true label.
Journal ArticleDOI
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning
TL;DR: In this paper , the authors argue that existing defenses can be broken by a simple adaptive attack, where a model trained on auxiliary data is able to invert gradients on both vision and language tasks.