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Found 28 papers, 12 papers with code
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting
Building on recent advancements in using linear models for time series, this paper introduces an LLM-enhanced mixture of linear experts for precise and efficient time series forecasting.
Heterophilic Graph Neural Networks Optimization with Causal Message-passing
We then present an analysis of decomposing the optimization target into a consistency penalty and a structure modification based on cause-effect relations.
Ranked #50 on
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on Squirrel
Graph Pre-Training Models Are Strong Anomaly Detectors
Graph Anomaly Detection (GAD) is a challenging and practical research topic where Graph Neural Networks (GNNs) have recently shown promising results.
MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification
To address these challenges, we introduce MCCoder, an LLM-powered system designed to generate code that addresses complex motion control tasks, with integrated soft-motion data verification.
Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation
Specifically, we construct the datasets by explicitly extracting users' positive and negative opinions from their post-purchase reviews using an LLM, and propose to evaluate systems based on whether the generated explanations 1) align well with the users' sentiments, and 2) accurately identify both positive and negative opinions of users on the target items.
Toward Physics-guided Time Series Embedding
This theory enables us to bypass the parameterized embedding layer and directly employ physical reconstruction techniques to acquire a data embedding representation.
Agent-Oriented Planning in Multi-Agent Systems
Through the collaboration of multiple agents possessing diverse expertise and tools, multi-agent systems achieve impressive progress in solving real-world problems.
Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast
Tensor decomposition has emerged as a prominent technique to learn low-dimensional representation under the supervision of reconstruction error, primarily benefiting data inference tasks like completion and imputation, but not classification task.
Parameter-Efficient Fine-Tuning via Circular Convolution
Low-Rank Adaptation (LoRA) has gained popularity for fine-tuning large foundation models, leveraging low-rank matrices $\mathbf{A}$ and $\mathbf{B}$ to represent weight changes (i. e., $\Delta \mathbf{W} = \mathbf{B} \mathbf{A}$).
DualTime: A Dual-Adapter Multimodal Language Model for Time Series Representation
In this study, based on the complementary information mining of time series multimodal data, we propose DualTime, a Dual-adapter multimodal language model for Time series representation implementing temporal-primary and textual-primary modeling simultaneously.
A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model
There are two main research lines, namely pre-training foundation models from scratch for time series and adapting large language foundation models for time series.
Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling
Moreover, a case study in the station clustering based on real passenger flow data is conducted, with quite valuable insights discovered.
Tensor-based process control and monitoring for semiconductor manufacturing with unstable disturbances
This paper proposes a novel process control and monitoring method for the complex structure of high-dimensional image-based overlay errors (modeled in tensor form), which are collected in semiconductor manufacturing processes.
An Incremental Unified Framework for Small Defect Inspection
Artificial Intelligence (AI)-driven defect inspection is pivotal in industrial manufacturing.
Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering
In this paper, we propose a novel tensor Dirichlet Process Multinomial Mixture model with graphs, which can preserve the hierarchical structure of the multi-dimensional trip information and cluster them in a unified one-step manner with the ability to determine the number of clusters automatically.
Deep Insights into Noisy Pseudo Labeling on Graph Data
We first present the error analysis of PL strategy by showing that the error is bounded by the confidence of PL threshold and consistency of multi-view prediction.
SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
Furthermore, we offer theoretical insights into SEGNO, highlighting that it can learn a unique trajectory between adjacent states, which is crucial for model generalization.
A Co-training Approach for Noisy Time Series Learning
Based on this, we create two views for the input time series through two different encoders.
Tensor Dirichlet Process Multinomial Mixture Model for Passenger Trajectory Clustering
In this paper, we propose a novel Tensor Dirichlet Process Multinomial Mixture model (Tensor-DPMM), which is designed to preserve the multi-mode and hierarchical structure of the multi-dimensional trip information via tensor, and cluster them in a unified one-step manner.
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping
We evaluated the effectiveness and flexibility of our representation learning framework on correlated time series forecasting and cold-start transferring the forecasting model to new instances with limited data.
Correlated Time Series Forecasting
Representation Learning
+1
MM-DAG: Multi-task DAG Learning for Multi-modal Data -- with Application for Traffic Congestion Analysis
This encourages the multi-task design: with each DAG as a task, the MM-DAG tries to learn the multiple DAGs jointly so that their consensus and consistency are maximized.
Data Imputation from the Perspective of Graph Dirichlet Energy
In our study, we examine this prevalent strategy through the lens of graph Dirichlet energy.
Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport
As the graphs to be aligned are usually constructed from different sources, the inconsistency issues of structures and features between two graphs are ubiquitous in real-world applications.
Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax
To address these challenges, we formulate the micro perspective mobility modeling into computing the relevance score between a diffusion and a location, conditional on a geometric graph.
Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
In this work, we present a regularized graph autoencoder for graph attribute imputation, named MEGAE, which aims at mitigating spectral concentration problem by maximizing the graph spectral entropy.
Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning
Graph self-supervised learning (SSL) has been vastly employed to learn representations from unlabeled graphs.
Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile
Spatiotemporal data is very common in many applications, such as manufacturing systems and transportation systems.
Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction
Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data.
