Search Results for author:
Found 33 papers, 11 papers with code
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks
Throughout our analysis, we connect our findings to previously-proposed hypotheses for the origins of degree bias, supporting and unifying some while drawing doubt to others.
On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method
Temporal Graph Learning (TGL) has become a prevalent technique across diverse real-world applications, especially in domains where data can be represented as a graph and evolves over time.
AI-based soundscape analysis: Jointly identifying sound sources and predicting annoyance
(4) Generalization tests show that the proposed model's ARP in the presence of model-unknown sound sources is consistent with expert expectations and can explain previous findings from the literature on sound-scape augmentation.
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?
Rewrite Caption Semantics: Bridging Semantic Gaps for Language-Supervised Semantic Segmentation
Such semantic misalignment circulates in pre-training, leading to inferior zero-shot performance in dense predictions due to insufficient visual concepts captured in textual representations.
Ensuring User-side Fairness in Dynamic Recommender Systems
In the ever-evolving landscape of user-item interactions, continual adaptation to newly collected data is crucial for recommender systems to stay aligned with the latest user preferences.
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation Learning
Sound events in daily life carry rich information about the objective world.
Soundscape attributes in Spanish: A comparison with the English version of the protocol proposed in Method A of the ISO/TS 12913–2
In soundscape studies there is a need to validate the ISO 12913 framework and questionnaire protocols in non-English speaking regions, which is being addressed via an international translation effort carried out within the Soundscape Attributes Translation Project (SATP).
BeMap: Balanced Message Passing for Fair Graph Neural Network
Fairness in graph neural networks has been actively studied recently.
CVGG-Net: Ship Recognition for SAR Images Based on Complex-Valued Convolutional Neural Network
Although convolutional neural networks have been successfully employed for SAR image target recognition, surpassing traditional algorithms, most existing research concentrates on the amplitude domain and neglects the essential phase information.
Bayesian Inference on Brain-Computer Interfaces via GLASS
Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers.
Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients
The National Lung Screening Trial (NLST) employed computed tomography texture analysis, which provides objective measurements of texture patterns on CT scans, to quantify the mortality risks of lung cancer patients.
Do We Really Need Complicated Model Architectures For Temporal Networks?
Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning.
Translation and cross-cultural adaptation methodology for soundscape attributes – A study with independent translation groups from English to Greek
The most important findings of this research are: the results of the bicultural translation group were closer to the PCA results of the English participants for every attribute, translation of bilingual groups may not be always sufficient, translation errors may be misinterpreted for cross-cultural differences without proper application of a translation methodology and the process of back translation can be effective, especially in cases where there are not corresponding words in the target language.
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks
The proposed JuryGCN is capable of quantifying uncertainty deterministically without modifying the GCN architecture or introducing additional parameters.
Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks
Deep neural network (DNN) models have achieved state-of-the-art predictive accuracy in a wide range of supervised learning applications.
Individualized Risk Assessment of Preoperative Opioid Use by Interpretable Neural Network Regression
Preoperative opioid use has been reported to be associated with higher preoperative opioid demand, worse postoperative outcomes, and increased postoperative healthcare utilization and expenditures.
RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network
Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications.
IMBENS: Ensemble Class-imbalanced Learning in Python
imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for leveraging the power of ensemble learning to address the class imbalance problem.
Robust High-Dimensional Regression with Coefficient Thresholding and its Application to Imaging Data Analysis
It is of importance to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible outliers in real-world applications such as imaging data analyses.
Adversarial Sample Detection for Speaker Verification by Neural Vocoders
We also show that the neural vocoder adopted in the detection framework is dataset-independent.
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.)
Image Response Regression via Deep Neural Networks
Delineating the associations between images and a vector of covariates is of central interest in medical imaging studies.
Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives
This chapter presents recent advances in content based image search and retrieval (CBIR) systems in remote sensing (RS) for fast and accurate information discovery from massive data archives.
Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration
Interferometric phase restoration has been investigated for decades and most of the state-of-the-art methods have achieved promising performances for InSAR phase restoration.
BigEarthNet Dataset with A New Class-Nomenclature for Remote Sensing Image Understanding
This is achieved by interpreting and arranging the CLC Level-3 nomenclature based on the properties of Sentinel-2 images in a new nomenclature of 19 classes.
So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification
This is especially true for an automated analysis of remote sensing images on a global scale, which enables us to address global challenges such as urbanization and climate change using state-of-the-art machine learning techniques.
BIG-bench Machine Learning
Cultural Vocal Bursts Intensity Prediction
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Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Data
Due to the ever-growing diversity of the data source, multi-modality feature learning has attracted more and more attention.
Covariance-Insured Screening
Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size.
Building Instance Classification Using Street View Images
The proposed method is based on Convolutional Neural Networks (CNNs) which classify facade structures from street view images, such as Google StreetView, in addition to remote sensing images which usually only show roof structures.
Classification with Ultrahigh-Dimensional Features
Although much progress has been made in classification with high-dimensional features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014}, classification with ultrahigh-dimensional features, wherein the features much outnumber the sample size, defies most existing work.
