MO1.R2.4

Frequency Domain Diffusion Model with Scale-Dependent Noise Schedule

Amir Ziashahabi, Baturalp Buyukates, University of Southern California, United States; Artan Sheshmani, Massachusetts Institute of Technology, United States; Yi-Zhuang You, University of California San Diego, United States; Salman Avestimehr, University of Southern California, United States
Session:
Topics in Machine Learning 1
Track:
8: Machine Learning
Location:
Ypsilon I-II-III
Presentation Time:
Mon, 8 Jul, 11:05 - 11:25
Session Chair:
Deniz Gündüz, Imperial College
Presentation
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Discussion
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Session MO1.R2
MO1.R2.1: Towards General Function Approximation in Nonstationary Reinforcement Learning
Songtao Feng, University of Florida, United States; Ming Yin, Princeton University, United States; Ruiquan Huang, Pennsylvania State University, United States; Yu-Xiang Wang, UC Santa Barbara, United States; Jing Yang, Pennsylvania State University, United States; Yingbin Liang, Ohio State University, United States
MO1.R2.2: Camouflage Adversarial Attacks on Multiple Agent Systems
Ziqing Lu, University of Iowa, United States; Guanlin Liu, Lifeng Lai, University of California Davis, United States; Weiyu Xu, University of Iowa, United States
MO1.R2.3: Erasure Coded Neural Network Inference via Fisher Averaging
Divyansh Jhunjhunwala, Neharika Jali, Gauri Joshi, Carnegie Mellon University, United States; Shiqiang Wang, IBM, United States
MO1.R2.4: Frequency Domain Diffusion Model with Scale-Dependent Noise Schedule
Amir Ziashahabi, Baturalp Buyukates, University of Southern California, United States; Artan Sheshmani, Massachusetts Institute of Technology, United States; Yi-Zhuang You, University of California San Diego, United States; Salman Avestimehr, University of Southern California, United States
Resources
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