Search Results for author:
Found 18 papers, 6 papers with code
AI Act for the Working Programmer
This paper aims at providing an aid for navigating the Act from the perspective of some professional in the software domain, termed "the working programmer", who feels the need to know about the stipulations of the Act.
IPA-NeRF: Illusory Poisoning Attack Against Neural Radiance Fields
In this study, we contribute to this area by introducing the Illusory Poisoning Attack against Neural Radiance Fields (IPA-NeRF).
Enhancing robustness of data-driven SHM models: adversarial training with circle loss
Structural health monitoring (SHM) is critical to safeguarding the safety and reliability of aerospace, civil, and mechanical infrastructure.
Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds
As with other ML tasks, classification models are notoriously brittle in the presence of adversarial attacks.
A Survey on Visual Mamba
This encompasses general visual tasks, Medical visual tasks (e. g., 2D / 3D segmentation, classification, and image registration, etc.
A Learning Paradigm for Interpretable Gradients
This paper studies interpretability of convolutional networks by means of saliency maps.
DP-Net: Learning Discriminative Parts for image recognition
This paper presents Discriminative Part Network (DP-Net), a deep architecture with strong interpretation capabilities, which exploits a pretrained Convolutional Neural Network (CNN) combined with a part-based recognition module.
CA-Stream: Attention-based pooling for interpretable image recognition
Explanations obtained from transformer-based architectures in the form of raw attention, can be seen as a class-agnostic saliency map.
P-Mamba: Marrying Perona Malik Diffusion with Mamba for Efficient Pediatric Echocardiographic Left Ventricular Segmentation
Specifically, we utilize the recently proposed ViM layers from the vision mamba to enhance our model's computational and memory efficiency while modeling global dependencies. In the DWT-based Perona-Malik Diffusion (PMD) Block, we devise a PMD Block for noise suppression while preserving the left ventricle's local shape cues.
Revisiting Transferable Adversarial Image Examples: Attack Categorization, Evaluation Guidelines, and New Insights
Transferable adversarial examples raise critical security concerns in real-world, black-box attack scenarios.
A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty
Aggregating distributed energy resources in power systems significantly increases uncertainties, in particular caused by the fluctuation of renewable energy generation.
Opti-CAM: Optimizing saliency maps for interpretability
Methods based on class activation maps (CAM) provide a simple mechanism to interpret predictions of convolutional neural networks by using linear combinations of feature maps as saliency maps.
Towards Good Practices in Evaluating Transfer Adversarial Attacks
In this work, we design good practices to address these limitations, and we present the first comprehensive evaluation of transfer attacks, covering 23 representative attacks against 9 defenses on ImageNet.
MOTSLAM: MOT-assisted monocular dynamic SLAM using single-view depth estimation
In this paper, we present MOTSLAM, a dynamic visual SLAM system with the monocular configuration that tracks both poses and bounding boxes of dynamic objects.
KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution
KDD CUP 2022 proposes a time-series forecasting task on spatial dynamic wind power dataset, in which the participants are required to predict the future generation given the historical context factors.
Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness
In this paper, we propose a framework of filter-based ensemble of deep neuralnetworks (DNNs) to defend against adversarial attacks.
Walking on the Edge: Fast, Low-Distortion Adversarial Examples
Adversarial examples of deep neural networks are receiving ever increasing attention because they help in understanding and reducing the sensitivity to their input.
