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Found 24 papers, 6 papers with code
Matrix Profile for Anomaly Detection on Multidimensional Time Series
This paper delves into the problem of anomaly detection in multidimensional time series, a common occurrence in real-world applications.
Preserving Individuality while Following the Crowd: Understanding the Role of User Taste and Crowd Wisdom in Online Product Rating Prediction
Our findings underscore the significance of individual user tastes in the context of online product rating prediction and the robustness of our approach across different model architectures.
A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining
Self-supervised Pretrained Models (PTMs) have demonstrated remarkable performance in computer vision and natural language processing tasks.
RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data
Spatial-temporal forecasting systems play a crucial role in addressing numerous real-world challenges.
Ranked #62 on
Time Series Forecasting
on ETTh1 (336) Multivariate
Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach
The emergence of pre-trained models has significantly impacted Natural Language Processing (NLP) and Computer Vision to relational datasets.
Ego-Network Transformer for Subsequence Classification in Time Series Data
The ego-networks of all subsequences collectively form a time series subsequence graph, and we introduce an algorithm to efficiently construct this graph.
Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insights
To facilitate this investigation, we introduce a CTSR benchmark dataset that comprises time series data from a variety of domains, such as motion, power demand, and traffic.
FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data
FATA-Trans is field- and time-aware for sequential tabular data.
An Efficient Content-based Time Series Retrieval System
A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing.
Multitask Learning for Time Series Data with 2D Convolution
In this paper, we investigate the application of MTL to the time series classification (TSC) problem.
Toward a Foundation Model for Time Series Data
A foundation model is a machine learning model trained on a large and diverse set of data, typically using self-supervised learning-based pre-training techniques, that can be adapted to various downstream tasks.
Revealing the Power of Masked Autoencoders in Traffic Forecasting
Our results on traffic benchmarks show that STMAE can largely enhance the forecasting capabilities of various spatial-temporal models.
Adversarial Collaborative Filtering for Free
In this paper, we present Sharpness-aware Collaborative Filtering (SharpCF), a simple yet effective method that conducts adversarial training without extra computational cost over the base optimizer.
EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding
The tool helps users discover nuance features of data entities, perform feature denoising/injecting in embedding training, and generate embeddings for unseen entities.
Sharpness-Aware Graph Collaborative Filtering
Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering.
PDT: Pretrained Dual Transformers for Time-aware Bipartite Graphs
Pre-training on large models is prevalent and emerging with the ever-growing user-generated content in many machine learning application categories.
Dynamic Graph Node Classification via Time Augmentation
Node classification on dynamic graphs is challenging for two reasons.
Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning
Whereas for the node injection attack, though being more practical, current approaches require training surrogate models to simulate a white-box setting, which results in significant performance downgrade when the surrogate architecture diverges from the actual victim model.
Boosting Graph Neural Networks via Adaptive Knowledge Distillation
In this paper, we propose a novel adaptive KD framework, called BGNN, which sequentially transfers knowledge from multiple GNNs into a student GNN.
Black-box Node Injection Attack for Graph Neural Networks
Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to vital fields that require high security standard such as product recommendation and traffic forecasting.
Adaptive Kernel Graph Neural Network
To solve this problem, in this paper, we propose a novel framework - i. e., namely Adaptive Kernel Graph Neural Network (AKGNN) - which learns to adapt to the optimal graph kernel in a unified manner at the first attempt.
Heterogeneous Temporal Graph Neural Network
To retain the heterogeneity, intra-relation aggregation is first performed over each slice of HTG to attentively aggregate information of neighbors with the same type of relation, and then intra-relation aggregation is exploited to gather information over different types of relations; to handle temporal dependencies, across-time aggregation is conducted to exchange information across different graph slices over the HTG.
heterogeneous temporal graph transformer: an intelligent system for evolving android malware detection
To capture malware evolution, we further consider the temporal dependence and introduce a heterogeneous temporal graph to jointly model malware propagation and evolution by considering heterogeneous spatial dependencies with temporal dimensions.
Automatic Opioid User Detection From Twitter: Transductive Ensemble Built On Different Meta-graph Based Similarities Over Heterogeneous Information Network
Opioid (e. g., heroin and morphine) addiction has become one of the largest and deadliest epidemics in the United States.
