most citedFreDF: Learning to Forecast in the Frequency Domain

7 citations · 8 across the 6 of their papers we have counts for

collaborators

7 papers

quant-ph2025

Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors

Wei-You Liao, Yuxuan Du, Xinbiao Wang +5

The ongoing development of quantum processors is driving breakthroughs in scientific discovery. Despite this progress, the formidable cost of fabricating large-scale quantum proces…

cs.CL2024

NoVo: Norm Voting off Hallucinations with Attention Heads in Large Language Models

Zheng Yi Ho, Siyuan Liang, Sen Zhang +2

Hallucinations in Large Language Models (LLMs) remain a major obstacle, particularly in high-stakes applications where factual accuracy is critical. While representation editing an…

cs.LG2024

Joint Input and Output Coordination for Class-Incremental Learning

Shuai Wang, Yibing Zhan, Yong Luo +4

Incremental learning is nontrivial due to severe catastrophic forgetting. Although storing a small amount of data on old tasks during incremental learning is a feasible solution, c…

cs.LG2024★ 1 cited

Towards Theoretical Understandings of Self-Consuming Generative Models

Shi Fu, Sen Zhang, Yingjie Wang +2

This paper tackles the emerging challenge of training generative models within a self-consuming loop, wherein successive generations of models are recursively trained on mixtures o…

cs.LG2024

Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases

Ziyi Zhang, Sen Zhang, Yibing Zhan +3

Bridging the gap between diffusion models and human preferences is crucial for their integration into practical generative workflows. While optimizing downstream reward models has…

cs.LG2024★ 7 cited

FreDF: Learning to Forecast in the Frequency Domain

Hao Wang, Licheng Pan, Zhichao Chen +6

Time series modeling presents unique challenges due to autocorrelation in both historical data and future sequences. While current research predominantly addresses autocorrelation…

cs.CV2024

TD^2-Net: Toward Denoising and Debiasing for Dynamic Scene Graph Generation

Xin Lin, Chong Shi, Yibing Zhan +3

Dynamic scene graph generation (SGG) focuses on detecting objects in a video and determining their pairwise relationships. Existing dynamic SGG methods usually suffer from several…