551 citations · 847 across the 23 of their papers we have counts for
8 papers · 1 filter
Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization Approach
Chao Chen, Tian Zhou, Yanjun Zhao +3
Spatio-temporal forecasting is crucial in various fields and requires a careful balance between identifying subtle patterns and filtering out noise. Vector quantization (VQ) appear…
Model-free Test Time Adaptation for Out-Of-Distribution Detection
YiFan Zhang, Xue Wang, Tian Zhou +5
Out-of-distribution (OOD) detection is essential for the reliability of ML models. Most existing methods for OOD detection learn a fixed decision criterion from a given in-distribu…
Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View
Peisong Niu, Tian Zhou, Xue Wang +2
In the burgeoning domain of Large Language Models (LLMs), there is a growing interest in applying LLM to time series forecasting, with multiple studies focused on leveraging textua…
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
Yi-Fan Zhang, Qingsong Wen, Xue Wang +6
Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data. Many algorithms a…
AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation
Yi-Fan Zhang, Xue Wang, Kexin Jin +5
Many recent machine learning tasks focus to develop models that can generalize to unseen distributions. Domain generalization (DG) has become one of the key topics in various field…
CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting
Wang Xue, Tian Zhou, Qingsong Wen +3
Recent studies have demonstrated the great power of Transformer models for time series forecasting. One of the key elements that lead to the transformer's success is the channel-in…
One Fits All:Power General Time Series Analysis by Pretrained LM
Tian Zhou, PeiSong Niu, Xue Wang +2
Although we have witnessed great success of pre-trained models in natural language processing (NLP) and computer vision (CV), limited progress has been made for general time series…
Free Lunch for Domain Adversarial Training: Environment Label Smoothing
YiFan Zhang, Xue Wang, Jian Liang +4
A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features…