Exploiting Explanations to Detect Misclassifications of Deep Learning Models in Power Grid Visual Inspection

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Recent works have identified a gap between research and practice in artificial intelligence security: threats studied in academia do not always reflect the practical use and security risks of AI. For example, while models are often studied in isolation, th ...

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Exploiting Explanations to Detect Misclassifications of Deep Learning Models in Power Grid Visual Inspection

Chattez avec Graph Search

Unlocking high-performance photocapacitors for edge computing in low-light environments

Aggregating Spatial and Photometric Context for Photometric Stereo

Reply to Dages et al: You AIn't using it right-artificial intelligence progress in allergy

Incorporating Projective Geometry into Deep Learning

Voices from the Frontline: Revealing the AI Practitioners' viewpoint on the European AI Act

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Predicting the long-term collective behaviour of fish pairs with deep learning

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Towards Trustworthy Deep Learning for Image Reconstruction

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Towards more Practical Threat Models in Artificial Intelligence Security

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life