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Found 11 papers, 7 papers with code
Improving and Unifying Discrete&Continuous-time Discrete Denoising Diffusion
Discrete diffusion models have seen a surge of attention with applications on naturally discrete data such as language and graphs.
Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation
Current graph diffusion models generate graphs in a one-shot fashion, but they require extra features and thousands of denoising steps to achieve optimal performance.
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).
Fast Unsupervised Deep Outlier Model Selection with Hypernetworks
Outlier detection (OD) finds many applications with a rich literature of numerous techniques.
From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management
Anomalies are often indicators of malfunction or inefficiency in various systems such as manufacturing, healthcare, finance, surveillance, to name a few.
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights.
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution
Outlier detection (OD) literature exhibits numerous algorithms as it applies to diverse domains.
PyGOD: A Python Library for Graph Outlier Detection
PyGOD is an open-source Python library for detecting outliers in graph data.
Causal inference using deep neural networks
Causal inference from observation data is a core problem in many scientific fields.
SUOD: Toward Scalable Unsupervised Outlier Detection
In this study, we propose a three-module acceleration framework called SUOD to expedite the training and prediction with a large number of unsupervised detection models.
Combining Machine Learning Models using combo Library
Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications.
