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Found 24 papers, 15 papers with code
A Review of Graph Neural Networks in Epidemic Modeling
In this paper, we endeavor to furnish a comprehensive review of GNNs in epidemic tasks and highlight potential future directions.
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
Time-series forecasting (TSF) finds broad applications in real-world scenarios.
A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation
In this survey, we aim to provide a comprehensive understanding of graph reduction methods, including graph sparsification, graph coarsening, and graph condensation.
Large Pre-trained time series models for cross-domain Time series analysis tasks
Large pre-trained models have been instrumental in significant advancements in domains like language and vision making model training for individual downstream tasks more efficient as well as provide superior performance.
PEMS: Pre-trained Epidemic Time-series Models
We tackle various important challenges specific to pre-training for epidemic time-series such as dealing with heterogeneous dynamics and efficiently capturing useful patterns from multiple epidemic datasets by carefully designing the SSL tasks to learn important priors about the epidemic dynamics that can be leveraged for fine-tuning to multiple downstream tasks.
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
We close both these gap and propose PROFHiT, which is a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
Performative Time-Series Forecasting
Time-series forecasting is a critical challenge in various domains and has witnessed substantial progress in recent years.
DF2: Distribution-Free Decision-Focused Learning
However, existing end-to-end DFL methods are hindered by three significant bottlenecks: model mismatch error, sample average approximation error, and gradient approximation error.
PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks
Physics-Informed Neural Networks (PINNs) have emerged as a promising deep learning framework for approximating numerical solutions to partial differential equations (PDEs).
Autoregressive Diffusion Model for Graph Generation
However, existing diffusion-based graph generative models are mostly one-shot generative models that apply Gaussian diffusion in the dequantized adjacency matrix space.
End-to-End Stochastic Optimization with Energy-Based Model
Decision-focused learning (DFL) was recently proposed for stochastic optimization problems that involve unknown parameters.
Differentiable Agent-based Epidemiology
Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of complex, dynamic infections under varying conditions and navigate uncertain environments.
Data-Centric Epidemic Forecasting: A Survey
The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole.
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
We close both these gap and propose PROFHiT, which is a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
EINNs: Epidemiologically-informed Neural Networks
We introduce EINNs, a framework crafted for epidemic forecasting that builds upon the theoretical grounds provided by mechanistic models as well as the data-driven expressibility afforded by AI models, and their capabilities to ingest heterogeneous information.
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
We use CAMul for multiple domains with varied sources and modalities and show that CAMul outperforms other state-of-art probabilistic forecasting models by over 25\% in accuracy and calibration.
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future
Our extensive experiments demonstrate that our method refines the performance of top models for COVID-19 forecasting, in contrast to non-trivial baselines, yielding 18% improvement over baselines, enabling us obtain a new SOTA performance.
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP, which directly models the probability density of the forecast value.
Incorporating Expert Guidance in Epidemic Forecasting
Forecasting influenza like illnesses (ILI) has rapidly progressed in recent years from an art to a science with a plethora of data-driven methods.
NetReAct: Interactive Learning for Network Summarization
The analyst can express her agreement/disagreement with the visualization of the network summary via iterative feedback, e. g. closing or moving documents ("nodes") together.
Mapping Network States Using Connectivity Queries
In contrast, we study the harder problem of inferring failed components given partial information of some `serviceable' reachable nodes and a small sample of point probes, being the first often more practical to obtain.
Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19
Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
Distributed Representation of Subgraphs
Motivated by the recent successes of embeddings in natural language processing, researchers have tried to find network embeddings in order to exploit machine learning algorithms for mining tasks like node classification and edge prediction.
Ranked #4 on
Malware Detection
on Android Malware Dataset
Distributed Representations of Signed Networks
Recent successes in word embedding and document embedding have motivated researchers to explore similar representations for networks and to use such representations for tasks such as edge prediction, node label prediction, and community detection.
