5 citations · 13 across the 17 of their papers we have counts for
17 papers
Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting
Yixin Wang, Yifan Hu, Peiyuan Liu +3
Time series forecasting remains a challenging problem due to the intricate entanglement of intra-period fluctuations and inter-period trends. While recent advances have attempted t…
Analytical Results for Two Exponential Family Distributions in Hierarchical Dirichlet Processes
Naiqi Li
The Hierarchical Dirichlet Process (HDP) provides a flexible Bayesian nonparametric framework for modeling grouped data with a shared yet unbounded collection of mixture components…
ProcGen3D: Learning Neural Procedural Graph Representations for Image-to-3D Reconstruction
Xinyi Zhang, Daoyi Gao, Naiqi Li +1
We introduce ProcGen3D, a new approach for 3D content creation by generating procedural graph abstractions of 3D objects, which can then be decoded into rich, complex 3D assets. In…
Logic-of-Thought: Empowering Large Language Models with Logic Programs for Solving Puzzles in Natural Language
Naiqi Li, Peiyuan Liu, Zheng Liu +3
Solving puzzles in natural language poses a long-standing challenge in AI. While large language models (LLMs) have recently shown impressive capabilities in a variety of tasks, the…
Efficient Differentiable Approximation of Generalized Low-rank Regularization
Naiqi Li, Yuqiu Xie, Peiyuan Liu +3
Low-rank regularization (LRR) has been widely applied in various machine learning tasks, but the associated optimization is challenging. Directly optimizing the rank function under…
FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting
Yifan Hu, Yuante Li, Peiyuan Liu +6
Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can be leveraged for profitable investmen…
FinMamba: Market-Aware Graph Enhanced Multi-Level Mamba for Stock Movement Prediction
Yifan Hu, Peiyuan Liu, Yuante Li +5
Recently, combining stock features with inter-stock correlations has become a common and effective approach for stock movement prediction. However, financial data presents signific…
TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting
Yifan Hu, Guibin Zhang, Peiyuan Liu +6
Time series forecasting methods generally fall into two main categories: Channel Independent (CI) and Channel Dependent (CD) strategies. While CI overlooks important covariate rela…
Diffusion Prior Interpolation for Flexibility Real-World Face Super-Resolution
Jiarui Yang, Tao Dai, Yufei Zhu +3
Diffusion models represent the state-of-the-art in generative modeling. Due to their high training costs, many works leverage pre-trained diffusion models' powerful representations…
DNF: Unconditional 4D Generation with Dictionary-based Neural Fields
Xinyi Zhang, Naiqi Li, Angela Dai
While remarkable success has been achieved through diffusion-based 3D generative models for shapes, 4D generative modeling remains challenging due to the complexity of object defor…
TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting
Peiyuan Liu, Beiliang Wu, Yifan Hu +4
Non-stationarity poses significant challenges for multivariate time series forecasting due to the inherent short-term fluctuations and long-term trends that can lead to spurious re…
Generative Pre-Trained Diffusion Paradigm for Zero-Shot Time Series Forecasting
Jiarui Yang, Tao Dai, Naiqi Li +6
In recent years, generative pre-trained paradigms such as Large Language Models (LLMs) and Large Vision Models (LVMs) have achieved revolutionary advancements and widespread real-w…
LCM: Locally Constrained Compact Point Cloud Model for Masked Point Modeling
Yaohua Zha, Naiqi Li, Yanzi Wang +6
The pre-trained point cloud model based on Masked Point Modeling (MPM) has exhibited substantial improvements across various tasks. However, these models heavily rely on the Transf…
CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning
Peiyuan Liu, Hang Guo, Tao Dai +5
Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a…
MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network
Yujun Huang, Bin Chen, Naiqi Li +3
Conventional compressed sensing (CS) algorithms typically apply a uniform sampling rate to different image blocks. A more strategic approach could be to allocate the number of meas…
WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting
Peiyuan Liu, Beiliang Wu, Naiqi Li +5
Recent CNN and Transformer-based models tried to utilize frequency and periodicity information for long-term time series forecasting. However, most existing work is based on Fourie…
SIAD: Self-supervised Image Anomaly Detection System
Jiawei Li, Chenxi Lan, Xinyi Zhang +7
Recent trends in AIGC effectively boosted the application of visual inspection. However, most of the available systems work in a human-in-the-loop manner and can not provide long-t…