Results 61 to 70 of about 160,235 (299)
AutoBayes: Automated Bayesian Graph Exploration for Nuisance- Robust Inference
IEEE Access, 2021 Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning.
Andac Demir +3 more
doaj +1 more source
Learnable Diffusion Framework for Mouse V1 Neural Decoding
Advanced Science, EarlyView.We introduce Sensorium‐Viz, a diffusion‐based framework for reconstructing high‐fidelity visual stimuli from mouse primary visual cortex activity. By integrating a novel spatial embedding module with a Diffusion Transformer (DiT) and a synthetic‐response augmentation strategy, our model outperforms state‐of‐the‐art fMRI‐based baselines, enabling robust
Kaiwen Deng +2 more
wiley +1 more source
How to beat a Bayesian adversary
European Journal of Applied MathematicsDeep neural networks and other modern machine learning models are often susceptible to adversarial attacks. Indeed, an adversary may often be able to change a model’s prediction through a small, directed perturbation of the model’s input – an issue in ...
Zihan Ding +3 more
doaj +1 more source
Adversarial attacks on medical machine learning
Science, 2019 Emerging vulnerabilities demand new ...
Finlayson, Samuel G. +5 more
openaire +4 more sources
Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019 Perceptual ad-blocking is a novel approach that detects online advertisements based on their visual content. Compared to traditional filter lists, the use of perceptual signals is believed to be less prone to an arms race with web publishers and ad networks. We demonstrate that this may not be the case.
Florian Tramèr +4 more
openaire +2 more sources
Advanced Science, EarlyView.This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo +6 more
wiley +1 more source
Diffusion–Model–Driven Discovery of Ferroelectrics for Photocurrent Applications
Advanced Science, EarlyView.We developed a diffusion model–based generative AI and high‐throughput screening framework that accelerates the discovery of photovoltaic ferroelectrics. By coupling AI driven crystal generation with machine learning and DFT screening, we identified Ca3P2 and LiCdP as new ferroelectric materials exhibiting strong polarization, feasible switching ...
Byung Chul Yeo +3 more
wiley +1 more source
Machine learning security and privacy:a survey
网络与信息安全学报, 2018 As an important method to implement artificial intelligence,machine learning technology is widely used in data mining,computer vision,natural language processing and other fields.With the development of machine learning,it brings amount of security and ...
Lei SONG,Chunguang MA,Guanghan DUAN
doaj +3 more sources
Adversarial Controls for Scientific Machine Learning
ACS Chemical Biology, 2018 New machine learning methods to analyze raw chemical and biological data are now widely accessible as open-source toolkits. This positions researchers to leverage powerful, predictive models in their own domains. We caution, however, that the application of machine learning to experimental research merits careful consideration.
Kangway V. Chuang, Michael J. Keiser
openaire +4 more sources
Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare
Advanced Science, EarlyView.Multimodal biosensing meets multidomain AI. Wearable biosensors capture complementary biochemical and physiological signals, while cross‐device, population‐aware learning aligns noisy, heterogeneous streams. This Review distills key sensing modalities, fusion and calibration strategies, and privacy‐preserving deployment pathways that transform ...
Chenshu Liu +10 more
wiley +1 more source