Results 51 to 60 of about 5,739,313 (302)
Attack and defence in cellular decision-making: lessons from machine learning
, 2019 Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition.
Bengio, Emmanuel +2 more
core +1 more source
Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning [PDF]
Computer Vision and Pattern Recognition, 2019 Most deraining works focus on rain streaks removal but they cannot deal adequately with heavy rain images. In heavy rain, streaks are strongly visible, dense rain accumulation or rain veiling effect significantly washes out the image, further scenes are ...
Ruoteng Li, L. Cheong, R. Tan
semanticscholar +1 more source
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 We consider the task of training classifiers without labels. We propose a weakly supervised method—adversarial label learning—that trains classifiers to perform well against an adversary that chooses labels for training data. The weak supervision constrains what labels the adversary can choose.
Arachie, Chidubem, Huang, Bert
openaire +3 more sources
Adversarial consistency and the uniqueness of the adversarial bayes classifier
European Journal of Applied MathematicsMinimizing an adversarial surrogate risk is a common technique for learning robust classifiers. Prior work showed that convex surrogate losses are not statistically consistent in the adversarial context – or in other words, a minimizing sequence of the ...
Natalie S. Frank
doaj +1 more source
Are Accuracy and Robustness Correlated?
, 2016 Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors.
Boult, Terrance E. +2 more
core +1 more source
Adversarial Learning for Product Recommendation [PDF]
AI, 2020 Product recommendation can be considered as a problem in data fusion—estimation of the joint distribution between individuals, their behaviors, and goods or services of interest. This work proposes a conditional, coupled generative adversarial network (RecommenderGAN) that learns to produce samples from a joint distribution between (view, buy ...
Bock, Joel R., Maewal, Akhilesh
openaire +2 more sources
Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems
, 2019 We show that end-to-end learning of communication systems through deep neural network (DNN) autoencoders can be extremely vulnerable to physical adversarial attacks.
Larsson, Erik G., Sadeghi, Meysam
core +1 more source
Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling
Advanced Healthcare Materials, EarlyView.Emerging advances in three‐dimensional bioprinting and computational modeling are reshaping cardiovascular (CV) research by enabling more realistic, patient‐specific tissue platforms. This review surveys cutting‐edge approaches that merge biomimetic CV constructs with computational simulations to overcome the limitations of traditional models, improve ...
Tanmay Mukherjee +7 more
wiley +1 more source
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks [PDF]
IEEE International Conference on Computer Vision, 2017 Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be
Jun-Yan Zhu +3 more
semanticscholar +1 more source
Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics
Advanced Materials, EarlyView.An active learning framework is introduced for the accelerated discovery of high‐entropy chalcogenides with superior thermoelectric performance. Only 80 targeted syntheses, selected from 16206 possible combinations, led to three high‐performance compositions, demonstrating the remarkable efficiency of data‐driven guidance in experimental materials ...
Hanhwi Jang +8 more
wiley +1 more source