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Found 54 papers, 20 papers with code
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.
Joint Optimization for Achieving Covertness in MIMO Over-the-Air Computation Networks
This paper investigates covert data transmission within a multiple-input multiple-output (MIMO) over-the-air computation (AirComp) network, where sensors transmit data to the access point (AP) while guaranteeing covertness to the warden (Willie).
TeaMs-RL: Teaching LLMs to Teach Themselves Better Instructions via Reinforcement Learning
The development of Large Language Models (LLMs) often confronts challenges stemming from the heavy reliance on human annotators in the reinforcement learning with human feedback (RLHF) framework, or the frequent and costly external queries tied to the self-instruct paradigm.
Exploring Fairness for FAS-assisted Communication Systems: from NOMA to OMA
This paper addresses the fairness issue within fluid antenna system (FAS)-assisted non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) systems, where a single fixed-antenna base station (BS) transmits superposition-coded signals to two users, each with a single fluid antenna.
Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
In long-term time series forecasting (LTSF) tasks, existing deep learning models overlook the crucial characteristic that discrete time series originate from underlying continuous dynamic systems, resulting in a lack of extrapolation and evolution capabilities.
The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes
Inspired by this, we introduce a new method for estimating the influence of training data, which requires calculating gradients for specific test samples, paired with a forward pass for each training point.
Position Paper: What Can Large Language Models Tell Us about Time Series Analysis
Time series analysis is essential for comprehending the complexities inherent in various real-world systems and applications.
Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values
However, real-world time series data is usually not well-structured, posting significant challenges to existing approaches: (1) The existence of missing values in multivariate time series data along variable and time dimensions hinders the effective modeling of interwoven spatial and temporal dependencies, resulting in important patterns being overlooked during model training; (2) Anomaly scoring with irregularly-sampled observations is less explored, making it difficult to use existing detectors for multivariate series without fully-observed values.
HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-Term Forecasting
Time series forecasting is crucial and challenging in the real world.
Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space
Domain generalization focuses on leveraging knowledge from multiple related domains with ample training data and labels to enhance inference on unseen in-distribution (IN) and out-of-distribution (OOD) domains.
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.
Proactive Monitoring via Jamming in Fluid Antenna Systems
Our objective is to maximize the average monitoring rate, whose expression involves the integral of the first-order Marcum $Q$ function.
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities.
Practical Membership Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study
Furthermore, our enhanced attacks outperform the baseline across multiple models and datasets, with the weakly supervised attack demonstrating an average-case performance improvement of $17\%$ and being at least $7$X more effective at low false-positive rates.
Message Passing Based Block Sparse Signal Recovery for DOA Estimation Using Large Arrays
This work deals with directional of arrival (DOA) estimation with a large antenna array.
A Human-on-the-Loop Optimization Autoformalism Approach for Sustainability
This paper outlines a natural conversational approach to solving personalized energy-related problems using large language models (LLMs).
Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models
Current literature, aiming to surpass the "Chain-of-Thought" approach, often resorts to an external modus operandi involving halting, modifying, and then resuming the generation process to boost Large Language Models' (LLMs) reasoning capacities.
WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine
We present WeaverBird, an intelligent dialogue system designed specifically for the finance domain.
Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection
To overcome these limitations, we propose a novel method, correlation-aware spatial-temporal graph learning (termed CST-GL), for time series anomaly detection.
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation.
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.
Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs
To better comprehend the complex physical laws, this paper proposes a novel learning based simulation model- Graph Networks with Spatial-Temporal neural Ordinary Equations (GNSTODE)- that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework.
How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?
In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs.
LAVA: Data Valuation without Pre-Specified Learning Algorithms
(1) We develop a proxy for the validation performance associated with a training set based on a non-conventional class-wise Wasserstein distance between training and validation sets.
Geometric Relational Embeddings: A Survey
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.
Hierarchical Multi-label Classification
Knowledge Graph Completion
+1
Monte Carlo Grid Dynamic Programming: Almost Sure Convergence and Probability Constraints
This paper presents a Monte Carlo-based sampling approach for the state space and an interpolation procedure for the resulting value function, dependent on the process noise density, in a "self-approximating" fashion, eliminating the need for ordering or set-membership tests.
Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance
Modern power systems will have to face difficult challenges in the years to come: frequent blackouts in urban areas caused by high power demand peaks, grid instability exacerbated by intermittent renewable generation, and global climate change amplified by rising carbon emissions.
On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds
We study the expressibility and learnability of convex optimization solution functions and their multi-layer architectural extension.
Non-stationary Risk-sensitive Reinforcement Learning: Near-optimal Dynamic Regret, Adaptive Detection, and Separation Design
We study risk-sensitive reinforcement learning (RL) based on an entropic risk measure in episodic non-stationary Markov decision processes (MDPs).
Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning
Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance.
Recognizing Nested Entities from Flat Supervision: A New NER Subtask, Feasibility and Challenges
However, existing nested NER models heavily rely on training data annotated with nested entities, while labeling such data is costly.
Variational Bayesian Inference Clustering Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC
Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference based clustering (VBIC) algorithm is developed to solve this clustering problem.
How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?
Most poisoning defenses presume access to a set of clean data (or base set).
Learning Neural Networks under Input-Output Specifications
In this paper, we examine an important problem of learning neural networks that certifiably meet certain specifications on input-output behaviors.
Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs
Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce.
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
To overcome the aforementioned problems, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme.
Adversarial Unlearning of Backdoors via Implicit Hypergradient
Particularly, its performance is more robust to the variation on triggers, attack settings, poison ratio, and clean data size.
Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs
Spatiotemporal representation learning on multivariate time series has received tremendous attention in forecasting traffic and energy data.
Towards General Robustness to Bad Training Data
In this paper, we focus on the problem of identifying bad training data when the underlying cause is unknown in advance.
Recurrent Neural Network Controllers Synthesis with Stability Guarantees for Partially Observed Systems
Neural network controllers have become popular in control tasks thanks to their flexibility and expressivity.
Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information.
MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition
Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation that the source has a certain marginal distribution, or increases the difficulty of adaptation.
A Unified Framework for Task-Driven Data Quality Management
High-quality data is critical to train performant Machine Learning (ML) models, highlighting the importance of Data Quality Management (DQM).
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning.
Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach
The trained DNNs can be driven by partial, noisy, or proxy descriptors of the current grid conditions.
Graph Self-Supervised Learning: A Survey
Deep learning on graphs has attracted significant interests recently.
Unitary Approximate Message Passing for Sparse Bayesian Learning
Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm.
Imitation Learning with Stability and Safety Guarantees
A method is presented to learn neural network (NN) controllers with stability and safety guarantees through imitation learning (IL).
Power up! Robust Graph Convolutional Network based on Graph Powering
By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.
Power up! Robust Graph Convolutional Network via Graph Powering
By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.
Stability-certified reinforcement learning: A control-theoretic perspective
We investigate the important problem of certifying stability of reinforcement learning policies when interconnected with nonlinear dynamical systems.
Inverse Reinforcement Learning via Deep Gaussian Process
We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations.
Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation
We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors.
