Search Results for author:
Found 38 papers, 14 papers with code
Robust Graph Representation Learning via Neural Sparsification
Graph representation learning serves as the core of important prediction tasks, ranging from product recommendation to fraud detection.
From Dense to Sparse: Event Response for Enhanced Residential Load Forecasting
To be specific, ERKG consists of knowledge extraction and guidance: i) a forecasting model is designed for the electricity usage events by estimating appliance operational states, aiming to extract the event-related sparse knowledge; ii) a novel knowledge-guided mechanism is established by fusing such state estimates of the appliance events into the RLF model, which can give particular focuses on the patterns of users' electricity consumption behaviors.
Learning System Dynamics without Forgetting
More importantly, we construct a novel benchmark of biological dynamic systems, featuring diverse systems with disparate dynamics and significantly enriching the research field of machine learning for dynamic systems.
International Trade Flow Prediction with Bilateral Trade Provisions
This paper presents a novel methodology for predicting international bilateral trade flows, emphasizing the growing importance of Preferential Trade Agreements (PTAs) in the global trade landscape.
Foundation Models for Time Series Analysis: A Tutorial and Survey
Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications.
Online GNN Evaluation Under Test-time Graph Distribution Shifts
This enables the effective evaluation of the well-trained GNNs' ability to capture test node semantics and structural representations, making it an expressive metric for estimating the generalization error in online GNN evaluation.
$\textbf{S}^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
To this end, we propose Semantic Space Informed Prompt learning with LLM ($S^2$IP-LLM) to align the pre-trained semantic space with time series embeddings space and perform time series forecasting based on learned prompts from the joint space.
Weakly Supervised Change Detection via Knowledge Distillation and Multiscale Sigmoid Inference
Moreover, we designed a Multiscale Sigmoid Inference (MSI) module as a post processing step to further refine the change probability map from the trained student network.
Structural Knowledge Informed Continual Multivariate Time Series Forecasting
To address this issue, we propose a novel Structural Knowledge Informed Continual Learning (SKI-CL) framework to perform MTS forecasting within a continual learning paradigm, which leverages structural knowledge to steer the forecasting model toward identifying and adapting to different regimes, and selects representative MTS samples from each regime for memory replay.
Continual Learning on Graphs: Challenges, Solutions, and Opportunities
To bridge the gap, we provide a comprehensive review of existing continual graph learning (CGL) algorithms by elucidating the different task settings and categorizing the existing methods based on their characteristics.
Empowering Time Series Analysis with Large Language Models: A Survey
Specifically, we first state the challenges and motivations of applying language models in the context of time series as well as brief preliminaries of LLMs.
Rank Supervised Contrastive Learning for Time Series Classification
Recently, various contrastive learning techniques have been developed to categorize time series data and exhibit promising performance.
Topology-aware Embedding Memory for Continual Learning on Expanding Networks
Memory replay based techniques have shown great success for continual learning with incrementally accumulated Euclidean data.
Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements
To address this issue, we introduce a novel approach to long-text classification and prediction.
Towards Safe Autonomy in Hybrid Traffic: Detecting Unpredictable Abnormal Behaviors of Human Drivers via Information Sharing
Then through extensive experiments on SUMO simulator, we show that our proposed algorithm has great detection performance in both highway and urban traffic.
Federated Distributionally Robust Optimization with Non-Convex Objectives: Algorithm and Analysis
Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e. g., network behavior analysis, risk management, etc.
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
To fill this gap, we review current state-of-the-art SSL methods for time series data in this article.
Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles
Deep learning is the method of choice for trajectory prediction for autonomous vehicles.
Asynchronous Distributed Bilevel Optimization
Bilevel optimization plays an essential role in many machine learning tasks, ranging from hyperparameter optimization to meta-learning.
Distributed Distributionally Robust Optimization with Non-Convex Objectives
Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e. g., network behavior analysis, risk management, etc.
Deep Federated Anomaly Detection for Multivariate Time Series Data
Specifically, we first design an Exemplar-based Deep Neural network (ExDNN) to learn local time series representations based on their compatibility with an exemplar module which consists of hidden parameters learned to capture varieties of normal patterns on each edge device.
Ordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Series
In this paper, we propose an ordered time series classification framework that is robust against missing classes in the training data, i. e., during testing we can prescribe classes that are missing during training.
Hierarchical Prototype Networks for Continual Graph Representation Learning
Despite significant advances in graph representation learning, little attention has been paid to the more practical continual learning scenario in which new categories of nodes (e. g., new research areas in citation networks, or new types of products in co-purchasing networks) and their associated edges are continuously emerging, causing catastrophic forgetting on previous categories.
Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection
Next, a dual discriminator based adversarial training procedure, which jointly considers an image discriminator that can maintain the local consistency at frame-level and a video discriminator that can enforce the global coherence of temporal dynamics, is employed to enhance the future frame prediction.
Hierarchical Prototype Network for Continual Graph Representation Learning
The key challenge is to incorporate the feature and topological information of new nodes in a continuous and effective manner such that performance over existing nodes is uninterrupted.
FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems
Moreover, we observe that open-set face recognition systems are more vulnerable than closed-set systems under different types of attacks.
Unsupervised Document Embedding via Contrastive Augmentation
We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner.
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series
Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications.
The Skill-Action Architecture: Learning Abstract Action Embeddings for Reinforcement Learning
Derived from a novel discovery that the SMDP option framework has an MDP equivalence, SA hierarchically extracts skills (abstract actions) from primary actions and explicitly encodes these knowledge into skill context vectors (embedding vectors).
Hierarchical Reinforcement Learning
reinforcement-learning
+1
TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series
To this end, we propose an autonomous representation learning approach for multivariate time series (TimeAutoML) with irregular sampling rates and variable lengths.
Inductive and Unsupervised Representation Learning on Graph Structured Objects
Inductive and unsupervised graph learning is a critical technique for predictive or information retrieval tasks where label information is difficult to obtain.
Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation
In practice, however, these two sets of reviews are notably different: users' reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item's reviews pertain only to that single item and are thus topically homogeneous.
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data
Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.
Learning Deep Network Representations with Adversarially Regularized Autoencoders
The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure.
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades.
Exemplar-Centered Supervised Shallow Parametric Data Embedding
Our strategy learns a shallow high-order parametric embedding function and compares training/test data only with learned or precomputed exemplars, resulting in a cost function with linear computational complexity for both training and testing.
A Shallow High-Order Parametric Approach to Data Visualization and Compression
These exemplars in combination with the feature mapping learned by HOPE effectively capture essential data variations.
Top Rank Supervised Binary Coding for Visual Search
In this paper, we propose a novel supervised binary coding approach, namely Top Rank Supervised Binary Coding (Top-RSBC), which explicitly focuses on optimizing the precision of top positions in a Hamming-distance ranking list towards preserving the supervision information.
