Zobrazeno 1 - 10
of 99
pro vyhledávání: '"No, Albert"'
Machine unlearning (MU) addresses privacy concerns by removing information of `forgetting data' samples from trained models. Typically, evaluating MU methods involves comparing unlearned models to those retrained from scratch without forgetting data,
Externí odkaz:
http://arxiv.org/abs/2405.17878
Current state-of-the-art diffusion models employ U-Net architectures containing convolutional and (qkv) self-attention layers. The U-Net processes images while being conditioned on the time embedding input for each sampling step and the class or capt
Externí odkaz:
http://arxiv.org/abs/2405.03958
We study $L_2$ mean estimation under central differential privacy and communication constraints, and address two key challenges: firstly, existing mean estimation schemes that simultaneously handle both constraints are usually optimized for $L_\infty
Externí odkaz:
http://arxiv.org/abs/2405.02341
Autor:
Lee, Haechang, Jeong, Wongi, Ryu, Dongil, Je, Hyunwoo, No, Albert, Kim, Kijeong, Chun, Se Young
Despite significant research on lightweight deep neural networks (DNNs) designed for edge devices, the current face detectors do not fully meet the requirements for "intelligent" CMOS image sensors (iCISs) integrated with embedded DNNs. These sensors
Externí odkaz:
http://arxiv.org/abs/2311.01001
Diffusion models have recently shown remarkable success in high-quality image generation. Sometimes, however, a pre-trained diffusion model exhibits partial misalignment in the sense that the model can generate good images, but it sometimes outputs u
Externí odkaz:
http://arxiv.org/abs/2307.02770
We study the mean estimation problem under communication and local differential privacy constraints. While previous work has proposed \emph{order}-optimal algorithms for the same problem (i.e., asymptotically optimal as we spend more bits), \emph{exa
Externí odkaz:
http://arxiv.org/abs/2306.04924
Autor:
Ignatov, Andrey, Timofte, Radu, Liu, Shuai, Feng, Chaoyu, Bai, Furui, Wang, Xiaotao, Lei, Lei, Yi, Ziyao, Xiang, Yan, Liu, Zibin, Li, Shaoqing, Shi, Keming, Kong, Dehui, Xu, Ke, Kwon, Minsu, Wu, Yaqi, Zheng, Jiesi, Fan, Zhihao, Wu, Xun, Zhang, Feng, No, Albert, Cho, Minhyeok, Chen, Zewen, Zhang, Xiaze, Li, Ran, Wang, Juan, Wang, Zhiming, Conde, Marcos V., Choi, Ui-Jin, Perevozchikov, Georgy, Ershov, Egor, Hui, Zheng, Dong, Mengchuan, Lou, Xin, Zhou, Wei, Pang, Cong, Qin, Haina, Cai, Mingxuan
The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-e
Externí odkaz:
http://arxiv.org/abs/2211.03885
Autor:
Gao, Yunfei1 (AUTHOR), No, Albert2 (AUTHOR) albertno@yonsei.ac.kr
Publikováno v:
BMC Bioinformatics. 10/1/2024, Vol. 25 Issue 1, p1-14. 14p.
Deep learning-based image signal processor (ISP) models for mobile cameras can generate high-quality images that rival those of professional DSLR cameras. However, their computational demands often make them unsuitable for mobile settings. Additional
Externí odkaz:
http://arxiv.org/abs/2203.04314
Publikováno v:
Published in International Conference on Machine Learning, 2022
The tremendous recent progress in analyzing the training dynamics of overparameterized neural networks has primarily focused on wide networks and therefore does not sufficiently address the role of depth in deep learning. In this work, we present the
Externí odkaz:
http://arxiv.org/abs/2202.02981