Search Results for author:
Found 16 papers, 6 papers with code
LossLens: Diagnostics for Machine Learning through Loss Landscape Visual Analytics
Modern machine learning often relies on optimizing a neural network's parameters using a loss function to learn complex features.
Visualizing Loss Functions as Topological Landscape Profiles
After describing this new topological landscape profile representation, we show how the shape of loss landscapes can reveal new details about model performance and learning dynamics, highlighting several use cases, including image segmentation (e. g., UNet) and scientific machine learning (e. g., physics-informed neural networks).
Evaluating Loss Landscapes from a Topology Perspective
Characterizing the loss of a neural network with respect to model parameters, i. e., the loss landscape, can provide valuable insights into properties of that model.
IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection
Effective fraud detection and analysis of government-issued identity documents, such as passports, driver's licenses, and identity cards, are essential in thwarting identity theft and bolstering security on online platforms.
Deceptive Fairness Attacks on Graphs via Meta Learning
We study deceptive fairness attacks on graphs to answer the following question: How can we achieve poisoning attacks on a graph learning model to exacerbate the bias deceptively?
GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation
As analysts create their spatial models, our framework flags potential issues with model parameter selections, utilizes template-based text generation to summarize model outputs, and links with external knowledge repositories to provide annotations that help to explain the model results.
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey
We systematically review related works from the data to the computational aspects.
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning
Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial capacity to represent rich attributes and relationships.
DISCO: Comprehensive and Explainable Disinformation Detection
Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed in numerous issues, such as political agendas and manipulating financial markets.
Towards Conditional Generation of Minimal Action Potential Pathways for Molecular Dynamics
In this paper, we utilized generative models, and reformulate it for problems in molecular dynamics (MD) simulation, by introducing an MD potential energy component to our generative model.
Deeper-GXX: Deepening Arbitrary GNNs
However, two major problems hinder the deeper GNNs to obtain satisfactory performance, i. e., vanishing gradient and over-smoothing.
Ranked #15 on
Node Classification
on Reddit
Metric Learning on Temporal Graphs via Few-Shot Examples
Graph metric learning methods aim to learn the distance metric over graphs such that similar graphs are closer and dissimilar graphs are farther apart.
InfoFair: Information-Theoretic Intersectional Fairness
The vast majority of the existing works on group fairness, with a few exceptions, primarily focus on debiasing with respect to a single sensitive attribute, despite the fact that the co-existence of multiple sensitive attributes (e. g., gender, race, marital status, etc.)
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes
Transfer Learning is intended to relax this assumption by modeling relationships between domains, and is often applied in deep learning applications to reduce the demand for labeled data and training time.
Diagnosing Concept Drift with Visual Analytics
Concept drift is a phenomenon in which the distribution of a data stream changes over time in unforeseen ways, causing prediction models built on historical data to become inaccurate.
Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Machine learning models are currently being deployed in a variety of real-world applications where model predictions are used to make decisions about healthcare, bank loans, and numerous other critical tasks.
