94 citations · 174 across the 15 of their papers we have counts for
16 papers
Paloma: Phase-Conditioned Residual Modulation for Time Series Forecasting
Jingru Fei, Wei Fan, Kun Yi +2
Explicitly modeling periodicity has proven to be an efficient approach for time series forecasting, where the residual, obtained after removing the periodic component from the time…
Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density
Jingru Fei, Kun Yi, Alex Xing Wang +3
Time series foundation models rely on large-scale pretraining over diverse datasets across domains, yet their heterogeneity in temporal patterns could hinder the effectiveness of t…
Improving Prediction Certainty Estimation for Reliable Early Exiting via Null Space Projection
Jianing He, Qi Zhang, Duoqian Miao +4
Early exiting has demonstrated great potential in accelerating the inference of pre-trained language models (PLMs) by enabling easy samples to exit at shallow layers, eliminating t…
Amplifier: Bringing Attention to Neglected Low-Energy Components in Time Series Forecasting
Jingru Fei, Kun Yi, Wei Fan +2
We propose an energy amplification technique to address the issue that existing models easily overlook low-energy components in time series forecasting. This technique comprises an…
MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification
Wei Fan, Jingru Fei, Dingyu Guo +5
Medical time series has been playing a vital role in real-world healthcare systems as valuable information in monitoring health conditions of patients. Accurate classification for…
FilterNet: Harnessing Frequency Filters for Time Series Forecasting
Kun Yi, Jingru Fei, Qi Zhang +4
While numerous forecasters have been proposed using different network architectures, the Transformer-based models have state-of-the-art performance in time series forecasting. Howe…
Robust Multivariate Time Series Forecasting against Intra- and Inter-Series Transitional Shift
Hui He, Qi Zhang, Kun Yi +4
The non-stationary nature of real-world Multivariate Time Series (MTS) data presents forecasting models with a formidable challenge of the time-variant distribution of time series,…
Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
Wei Fan, Kun Yi, Hangting Ye +3
While most time series are non-stationary, it is inevitable for models to face the distribution shift issue in time series forecasting. Existing solutions manipulate statistical me…
MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization
Yu Zhang, Qi Zhang, Zixuan Gong +9
Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success, leading to rapid advancements in multimodal studies. However, CLIP faces a notable challenge in terms…
HyDiscGAN: A Hybrid Distributed cGAN for Audio-Visual Privacy Preservation in Multimodal Sentiment Analysis
Zhuojia Wu, Qi Zhang, Duoqian Miao +3
Multimodal Sentiment Analysis (MSA) aims to identify speakers' sentiment tendencies in multimodal video content, raising serious concerns about privacy risks associated with multim…
Wills Aligner: Multi-Subject Collaborative Brain Visual Decoding
Guangyin Bao, Qi Zhang, Zixuan Gong +6
Decoding visual information from human brain activity has seen remarkable advancements in recent research. However, the diversity in cortical parcellation and fMRI patterns across…
Deep Coupling Network For Multivariate Time Series Forecasting
Kun Yi, Qi Zhang, Hui He +4
Multivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- a…
IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting
Wei Fan, Shun Zheng, Pengyang Wang +5
Due to the non-stationarity of time series, the distribution shift problem largely hinders the performance of time series forecasting. Existing solutions either rely on using certa…
Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector
An Lao, Qi Zhang, Chongyang Shi +4
Multimodal content, such as mixing text with images, presents significant challenges to rumor detection in social media. Existing multimodal rumor detection has focused on mixing t…
FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
Kun Yi, Qi Zhang, Wei Fan +6
Multivariate time series (MTS) forecasting has shown great importance in numerous industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods usually…
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
Kun Yi, Qi Zhang, Wei Fan +7
Time series forecasting has played the key role in different industrial, including finance, traffic, energy, and healthcare domains. While existing literatures have designed many s…