Zobrazeno 1 - 10
of 2 021
pro vyhledávání: '"Guo, Chuan"'
Autor:
Thiang, Guo Chuan
We review the differential topology underlying the topological protection of energy band crossings in Weyl semimetals, and how they lead to the experimental signature of surface Fermi arcs.
Comment: 11 pages, 2 figures, Contribution to Encyclope
Comment: 11 pages, 2 figures, Contribution to Encyclope
Externí odkaz:
http://arxiv.org/abs/2407.12692
Autor:
Mu, Yuxuan, Zuo, Xinxin, Guo, Chuan, Wang, Yilin, Lu, Juwei, Wu, Xiaofeng, Xu, Songcen, Dai, Peng, Yan, Youliang, Cheng, Li
We present GSD, a diffusion model approach based on Gaussian Splatting (GS) representation for 3D object reconstruction from a single view. Prior works suffer from inconsistent 3D geometry or mediocre rendering quality due to improper representations
Externí odkaz:
http://arxiv.org/abs/2407.04237
Autor:
Bordes, Florian, Pang, Richard Yuanzhe, Ajay, Anurag, Li, Alexander C., Bardes, Adrien, Petryk, Suzanne, Mañas, Oscar, Lin, Zhiqiu, Mahmoud, Anas, Jayaraman, Bargav, Ibrahim, Mark, Hall, Melissa, Xiong, Yunyang, Lebensold, Jonathan, Ross, Candace, Jayakumar, Srihari, Guo, Chuan, Bouchacourt, Diane, Al-Tahan, Haider, Padthe, Karthik, Sharma, Vasu, Xu, Hu, Tan, Xiaoqing Ellen, Richards, Megan, Lavoie, Samuel, Astolfi, Pietro, Hemmat, Reyhane Askari, Chen, Jun, Tirumala, Kushal, Assouel, Rim, Moayeri, Mazda, Talattof, Arjang, Chaudhuri, Kamalika, Liu, Zechun, Chen, Xilun, Garrido, Quentin, Ullrich, Karen, Agrawal, Aishwarya, Saenko, Kate, Celikyilmaz, Asli, Chandra, Vikas
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guide us through unfamiliar environments to generative models that produce
Externí odkaz:
http://arxiv.org/abs/2405.17247
While recently Large Language Models (LLMs) have achieved remarkable successes, they are vulnerable to certain jailbreaking attacks that lead to generation of inappropriate or harmful content. Manual red-teaming requires finding adversarial prompts t
Externí odkaz:
http://arxiv.org/abs/2404.16873
Protecting privacy during inference with deep neural networks is possible by adding noise to the activations in the last layers prior to the final classifiers or other task-specific layers. The activations in such layers are known as "features" (or,
Externí odkaz:
http://arxiv.org/abs/2404.02866
Autor:
Lebensold, Jonathan, Sanjabi, Maziar, Astolfi, Pietro, Romero-Soriano, Adriana, Chaudhuri, Kamalika, Rabbat, Mike, Guo, Chuan
Text-to-image diffusion models have been shown to suffer from sample-level memorization, possibly reproducing near-perfect replica of images that they are trained on, which may be undesirable. To remedy this issue, we develop the first differentially
Externí odkaz:
http://arxiv.org/abs/2403.14421
Data-dependent privacy accounting frameworks such as per-instance differential privacy (pDP) and Fisher information loss (FIL) confer fine-grained privacy guarantees for individuals in a fixed training dataset. These guarantees can be desirable compa
Externí odkaz:
http://arxiv.org/abs/2403.05598
Autor:
Sander, Tom, Yu, Yaodong, Sanjabi, Maziar, Durmus, Alain, Ma, Yi, Chaudhuri, Kamalika, Guo, Chuan
Differentially private (DP) machine learning is considered the gold-standard solution for training a model from sensitive data while still preserving privacy. However, a major barrier to achieving this ideal is its sub-optimal privacy-accuracy trade-
Externí odkaz:
http://arxiv.org/abs/2403.02506
Vision-Language Models (VLMs) have emerged as the state-of-the-art representation learning solution, with myriads of downstream applications such as image classification, retrieval and generation. A natural question is whether these models memorize t
Externí odkaz:
http://arxiv.org/abs/2402.02103
Human motion stylization aims to revise the style of an input motion while keeping its content unaltered. Unlike existing works that operate directly in pose space, we leverage the latent space of pretrained autoencoders as a more expressive and robu
Externí odkaz:
http://arxiv.org/abs/2401.13505