Results 241 to 250 of about 5,739,313 (302)

Integrating multimodal data and machine learning for entrepreneurship research

Strategic Entrepreneurship Journal, EarlyView.
Abstract Research Summary Extant research in neuroscience suggests that human perception is multimodal in nature—we model the world integrating diverse data sources such as sound, images, taste, and smell. Working in a dynamic environment, entrepreneurs are expected to draw on multimodal inputs in their decision making.
Yash Raj Shrestha, Vivianna Fang He
wiley   +1 more source

How to grow new applications out of old research? Evidence from firm cumulative investments in deep learning

Strategic Management Journal, EarlyView.
Abstract Research Summary Firm technological research has the potential to spawn multiple applications. Despite recognizing such potential, past literature disagrees on the process through which firms discover and grow new applications out of their past technological research.
Xirong (Subrina) Shen
wiley   +1 more source

Lifecycle‐Based Governance to Build Reliable Ethical AI Systems

Systems Research and Behavioral Science, EarlyView.
ABSTRACT Artificial intelligence (AI) systems represent a paradigm shift in technological capabilities, offering transformative potential across industries while introducing novel governance and implementation challenges. This paper presents a comprehensive framework for understanding AI systems through three critical dimensions: trustworthiness ...
Maikel Leon
wiley   +1 more source

Mission Aware Cyber‐Physical Security

Systems Engineering, EarlyView.
ABSTRACT Perimeter cybersecurity, while essential, has proven insufficient against sophisticated, coordinated, and cyber‐physical attacks. In contrast, mission‐centric cybersecurity emphasizes finding evidence of attack impact on mission success, allowing for targeted resource allocation to mitigate vulnerabilities and protect critical assets.
Georgios Bakirtzis, Bryan Carter, Cody H. Fleming, Carl R. Elks   +3 more
wiley   +1 more source

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Generative Adversarial Networks

International Conference on Computing Communication and Networking Technologies, 2021
Generative Adversarial Networks (GANs) are a type of deep learning techniques that have shown remarkable success in generating realistic images, videos, and other types of data.
I. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio   +7 more
semanticscholar   +1 more source

Adversarial Invariant Learning

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Though machine learning algorithms are able to achieve pattern recognition from the correlation between data and labels, the presence of spurious features in the data decreases the robustness of these learned relationships with respect to varied testing environments. This is known as out-of-distribution (OoD) generalization problem. Recently, invariant
Nanyang Ye, Jingxuan Tang, Huayu Deng, Xiao-Yun Zhou, Qianxiao Li, Zhenguo Li, Guang-Zhong Yang, Zhanxing Zhu   +7 more
openaire   +1 more source

Artifacts-Disentangled Adversarial Learning for Deepfake Detection

IEEE transactions on circuits and systems for video technology (Print), 2023
Due to the development of facial manipulation technologies, the generated deepfake videos cause a severe trust crisis in society. Existing methods prove that effective extraction of the artifacts introduced during the forgery process is essential for ...
Xin Li, R. Ni, Pengpeng Yang, Zhiqiang Fu, Yao Zhao   +4 more
semanticscholar   +1 more source

MADG: Margin-based Adversarial Learning for Domain Generalization

Neural Information Processing Systems, 2023
Domain Generalization (DG) techniques have emerged as a popular approach to address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing well to the target domain unseen during the training.
Aveen Dayal, B. VimalK., Linga Reddy Cenkeramaddi, C. K. Mohan, Abhinav Kumar, Vineeth N. Balasubramanian   +5 more
semanticscholar   +1 more source

Weakly Supervised Semantic Segmentation via Adversarial Learning of Classifier and Reconstructor

Computer Vision and Pattern Recognition, 2023
In Weakly Supervised Semantic Segmentation (WSSS), Class Activation Maps (CAMs) usually 1) do not cover the whole object and 2) be activated on irrelevant regions.
H. Kweon, Sung-Hoon Yoon, Kuk-Jin Yoon
semanticscholar   +1 more source

Experimental quantum adversarial learning with programmable superconducting qubits

Nature Computational Science, 2022
Quantum computing promises to enhance machine learning and artificial intelligence. However, recent theoretical works show that, similar to traditional classifiers based on deep classical neural networks, quantum classifiers would suffer from adversarial
W. Ren, Weikang Li, Shibo Xu, Ke Wang, Wenjie Jiang, Feitong Jin, Xuhao Zhu, Jiachen Chen, Zixuan Song, P. Zhang, Hang Dong, Xu Zhang, Jinfeng Deng, Yu Gao, Chuanyu Zhang, Yaozu Wu, Bing Zhang, Q. Guo, Hekang Li, Zhen Wang, J. Biamonte, Chao Song, D. Deng, H. Wang   +23 more
semanticscholar   +1 more source