1 code implementation • 22 Feb 2024 • Yujia Huang, Adishree Ghatare, Yuanzhe Liu, Ziniu Hu, Qinsheng Zhang, Chandramouli S Sastry, Siddharth Gururani, Sageev Oore, Yisong Yue
We propose Stochastic Control Guidance (SCG), a novel guidance method that only requires forward evaluation of rule functions that can work with pre-trained diffusion models in a plug-and-play way, thus achieving training-free guidance for non-differentiable rules for the first time.
1 code implementation • 30 Oct 2022 • Yujia Huang, Ivan Dario Jimenez Rodriguez, huan zhang, Yuanyuan Shi, Yisong Yue
Forward invariance is a long-studied property in control theory that is used to certify that a dynamical system stays within some pre-specified set of states for all time, and also admits robustness guarantees (e. g., the certificate holds under perturbations).
2 code implementations • 16 May 2022 • Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, Anima Anandkumar
Adversarial purification refers to a class of defense methods that remove adversarial perturbations using a generative model.
1 code implementation • NeurIPS 2021 • Yujia Huang, huan zhang, Yuanyuan Shi, J Zico Kolter, Anima Anandkumar
Certified robustness is a desirable property for deep neural networks in safety-critical applications, and popular training algorithms can certify robustness of a neural network by computing a global bound on its Lipschitz constant.
1 code implementation • NeurIPS 2020 • Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan Nguyen, Doris Y. Tsao, Anima Anandkumar
This mechanism can be interpreted as a form of self-consistency between the maximum a posteriori (MAP) estimation of an internal generative model and the external environment.
no code implementations • NeurIPS 2019 Workshop Neuro AI 2019 • Yujia Huang, Sihui Dai, Tan Nguyen, Pinglei Bao, Doris Y. Tsao, Richard G. Baraniuk, Anima Anandkumar
Primates have a remarkable ability to correctly classify images even in the presence of significant noise and degradation.
no code implementations • 10 Jul 2019 • Yujia Huang, Sihui Dai, Tan Nguyen, Richard G. Baraniuk, Anima Anandkumar
Our results show that when trained on CIFAR-10, lower likelihood (of latent variables) is assigned to SVHN images.
no code implementations • 31 Mar 2017 • Wei-Sheng Lai, Yujia Huang, Neel Joshi, Chris Buehler, Ming-Hsuan Yang, Sing Bing Kang
We present a system for converting a fully panoramic ($360^\circ$) video into a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience.
no code implementations • 26 Jan 2017 • Liang Zheng, Yujia Huang, Huchuan Lu, Yi Yang
Second, to reduce the impact of pose estimation errors and information loss during PoseBox construction, we design a PoseBox fusion (PBF) CNN architecture that takes the original image, the PoseBox, and the pose estimation confidence as input.