Search Results for author:
Found 8 papers, 3 papers with code
LCM: Locally Constrained Compact Point Cloud Model for Masked Point Modeling
Considering the varying information density between masked and unmasked patches in the decoder inputs of MPM, we introduce a locally constrained Mamba-based decoder.
CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning
Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data.
Knowledge Distillation
Multivariate Time Series Forecasting
+2
MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network
In this paper, we propose a Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network (MB-RACS) framework, which aims to adaptively determine the sampling rate for each image block in accordance with traditional measurement bounds theory.
WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting
In this paper, we propose a Wavelet-Fourier Transform Network (WFTNet) for long-term time series forecasting.
Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model
Unsupervised surface anomaly detection aims at discovering and localizing anomalous patterns using only anomaly-free training samples.
SIAD: Self-supervised Image Anomaly Detection System
To make a step forward, this paper outlines an automatic annotation system called SsaA, working in a self-supervised learning manner, for continuously making the online visual inspection in the manufacturing automation scenarios.
Deep Dirichlet Process Mixture Models
In this paper we propose the deep Dirichlet process mixture (DDPM) model, which is an unsupervised method that simultaneously performs clustering and feature learning.
Stochastic Deep Gaussian Processes over Graphs
In this paper we propose Stochastic Deep Gaussian Processes over Graphs (DGPG), which are deep structure models that learn the mappings between input and output signals in graph domains.
