Search Results for author:
Found 8 papers, 4 papers with code
Graph isomorphism UNet
Recent methods leverage learnable parameters to extract structural information from neural networks and extend pooling and unpooling to graphs using node features and graph structural information.
Ranked #2 on
Graph Classification
on PTC
Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN
This study presents an adversarial method for anomaly detection in real-world applications, leveraging the power of generative adversarial neural networks (GANs) through cycle consistency in reconstruction error.
Ranked #1 on
Anomaly Detection
on SVHN
Layer-wise Regularized Adversarial Training using Layers Sustainability Analysis (LSA) framework
This paper introduces a novel framework (Layer Sustainability Analysis (LSA)) for the analysis of layer vulnerability in an arbitrary neural network in the scenario of adversarial attacks.
Limited Budget Adversarial Attack Against Online Image Stream
An adversary wants to attack a limited number of images within a stream of known length to reduce the exposure risk.
Color Texture Image Retrieval Based on Copula Multivariate Modeling in the Shearlet Domain
In the proposed framework, Gaussian Copula is used to model the dependencies between different sub-bands of the Non Subsample Shearlet Transform (NSST) and non-Gaussian models are used for marginal modeling of the coefficients.
A Distributed Approximate Nearest Neighbor Method for Real-Time Face Recognition
Nowadays, face recognition and more generally image recognition have many applications in the modern world and are widely used in our daily tasks.
Reconstruction of Gene Regulatory Networks usingMultiple Datasets
With a motivation to compensate for this shortage, we developed an algorithm called GENEREF that can accumulate information from multiple types of data sets in an iterative manner, with each iteration boosting the performance of the prediction results.
Unsupervised Hypergraph Feature Selection via a Novel Point-Weighting Framework and Low-Rank Representation
In this paper, we propose an unsupervised hypergraph feature selection method via a novel point-weighting framework and low-rank representation that captures the importance of different data points.
