16 accepted papers
Accurate estimation of time-varying treatment effects is crucial for optimizing interventions in personalized medicine. However, observational data often contains complex confounding bias and temporal complexities, making counterfactual estimation challenging. We propose Counterfactual Estimation vi
Zero-shot classifier expansion aims to adapt existing model to new, unseen classes. It utilizes class attributes or textual descriptions to learn a mapping from the semantic space to the classifier
Fault Diagnosis (FD) on sequential data suffers from irregular sampling (with missing values), limited training data, and varying underlying environments. In response, this paper proposes FD by adjoint learning in continuous-time model space. Model-Space Learning employs well-fitted models that capt
Zero-shot classifier expansion aims to recognize unseen classes by learning a shared mechanism to map semantics of all classes to classifier weights without access to images. However, existing methods rely on a shared mapping, which is difficult to classify in scenarios containing a mixture of disti…
Multivariate time series classification (MTSC) has broad applications in numerous domains. Existing MTSC methods typically focus on either temporal dynamics or variable interactions of the data, often overlooking cross-scale couplings among different variables. To bridge this gap, we propose Scale-V
Efficient anomaly detection of irregular sequences, especially those characterized by non-uniform sampling from discontinuous operations or unreliable sensors, presents challenges across various fields. In response, this paper introduces irregular-sequence classification in ''Ct-Echo Model Space''.…
Zero-shot learning has shown significant potential for creating cost-effective and flexible systems to expand classifiers to new categories. However, existing methods still rely on manually created attributes designed by domain experts. Motivated by the widespread success of large language models (L
Fault Diagnosis (FD) in time-varying data presents considerations such as limited training data, intra- and inter-dimensional correlations, and constraints of training time. In response, this paper introduces FD in the Reservoir-Embedded-Directional Network (REDNet) model space. Model-oriented metho
Anomaly detection in sequential signals is gaining prominence, especially with limited training data and timeliness requirements. Fully extracting the data-inside changing information, we propose a novel Wavelet-Enhanced Reservoir Computing framework (WE-Res). Our framework uses Discrete Wavelet Tra…
Fault Diagnosis (FD) in time-varying systems faces challenges like limited training data, varying environments, and timeliness. Building upon the framework of model-space learning (MSL), we introduce co-objective learning in Dynamic-Integration network (DynInt) model space as a solution for FD. MSL…
Learning the causality between variables, known as DAG structure learning, is critical yet challenging due to issues such as insufficient data and noise. While prior knowledge can improve the learning process and refine the DAG structure, incorporating prior knowledge is not without pitfalls. In par…
Analyzing inherent temporal dynamics is a critical pathway for time series classification, where Reservoir Computing (RC) exhibits effectiveness and high efficiency. However, typical RC considers recursive updates from adjacent states, struggling with long-term dependencies. In response, this paper…
Ground Penetrating Radar (GPR) provides detailed subterranean insights. Nevertheless, underground diagnosis via GPR is hindered by the fact that training data typically contain only normal samples, along with the complexity of GPR data’s wave-collection characteristics. This paper proposes subsurfac
A key challenge in personalized healthcare is identifying optimal intervention sequences to guide temporal systems toward target outcomes, a novel problem we formalize as counterfactual target achievement. In addressing this problem, directly adopting counterfactual estimation methods face compoundi…
With the rapid growth of audio data, there's a pressing need for automatic audio classification. As a type of time-series data, audio exhibits waveform fluctuations in both the time and frequency domains that evolve over time, with similar instances sharing consistent patterns. This study introduces…
Three-dimensional Ground Penetrating Radar (3D GPR) data offer comprehensive views of the subsurface, yet identifying and classifying underground anomalies from this data is challenging due to limitations like scarce training data and variable underground environments. In response, we introduce lear…