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Found 10 papers, 1 papers with code
On the notion of Hallucinations from the lens of Bias and Validity in Synthetic CXR Images
from the lens of bias and validity of the generated outcomes.
Data-Free Model Extraction Attacks in the Context of Object Detection
A significant number of machine learning models are vulnerable to model extraction attacks, which focus on stealing the models by using specially curated queries against the target model.
Targeted Attacks on Timeseries Forecasting
In this paper, we propose a novel formulation of Directional, Amplitudinal, and Temporal targeted adversarial attacks on time series forecasting models.
Critical Checkpoints for Evaluating Defence Models Against Adversarial Attack and Robustness
From past couple of years there is a cycle of researchers proposing a defence model for adversaries in machine learning which is arguably defensible to most of the existing attacks in restricted condition (they evaluate on some bounded inputs or datasets).
Emerging AI Security Threats for Autonomous Cars -- Case Studies
Artificial Intelligence has made a significant contribution to autonomous vehicles, from object detection to path planning.
Mapping of Real World Problems to Nature Inspired Algorithm using Goal based Classification and TRIZ
The technologies are capable enough to solve technically challenging and complex problems which seemed impossible task.
NodeNet: A Graph Regularised Neural Network for Node Classification
However, the majority of AI/ML techniques leave out the linkages among data points.
Ranked #7 on
Node Classification
on Pubmed
Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms
Synthetic Aperture Radar (SAR) images contain a huge amount of information, however, the number of practical use-cases is limited due to the presence of speckle noise in them.
A Novel Framework for Selection of GANs for an Application
We also provide a comparison of various GANs from the application point of view, its behaviour and implementation details.
Eigenvector Component Calculation Speedup over NumPy for High-Performance Computing
Applications related to artificial intelligence, machine learning, and system identification simulations essentially use eigenvectors.
Performance Distributed, Parallel, and Cluster Computing Data Structures and Algorithms
