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Hybrid Deep Learning-Machine Learning Fusion of Clinical, Radiomic and Deep Learning Features for Preoperative Differentiation of Solitary Pulmonary Mucinous Adenocarcinoma. [PDF]
Diagnostics (Basel)Sun C, Sun J, Wei F, Yang S, Ba W, Li Y.
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Physics-Guided Dual-Branch Fusion Model for High-Resolution Range Profile Target Recognition. [PDF]
Sensors (Basel)Xia Z, Wu M, Xiao F, Liu H.
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Distributed Deep Learning for IoT
2022Distributed deep learning is a type of machine learning that uses neural networks to learn and make predictions at scale. This is achieved by having many different computer systems that are connected via the internet. This allows for more parallel processing and faster results.
Amuthan Nallathambi +2 more
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Deep Conditional Distribution Learning for Age Estimation
IEEE Transactions on Information Forensics and Security, 2021Age estimation is a challenging task not only because face appearance is affected by illumination, pose, and expression, but also because there exists age label ambiguity among different demographic groups. In this work, we first revisit different label distribution learning (LDL) based age estimation methods and propose a more general formulation ...
Haomiao Sun +3 more
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Deep Osmosis: Holistic Distributed Deep Learning in Osmotic Computing
IEEE Cloud Computing, 2017Emerging availability (and varying complexity and types) of Internet of Things (IoT) devices, along with large data volumes that such devices (can potentially) generate, can have a significant impact on our lives, fuelling the development of critical next-generation services and applications in a variety of application domains (e.g.
Ahsan Morshed +5 more
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Distributed Emergent Agreements with Deep Reinforcement Learning
2021 International Joint Conference on Neural Networks (IJCNN), 2021Building autonomous agents that are capable to cooperate with other machines is an essential step towards large scale application of AI systems. Especially systems comprised of multiple self-interested agents with general sum returns can profit from cooperative behavior as cooperation can help to increase the return from all agents simultaneously.
Kyrill Schmid +4 more
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Performance Analysis of Distributed and Scalable Deep Learning
2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), 2020With renewed global interest for Artificial Intelligence (AI) methods, the past decade has seen a myriad of new programming models and tools that enable better and faster Machine Learning (ML). More recently, a subset of ML known as Deep Learning (DL) raised an increased interest due to its inherent ability to tackle efficiently novel cognitive ...
Sean Mahon +4 more
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Privacy Preserving Deep Learning with Distributed Encoders
2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019In this paper, we propose a distributed machine learning framework for training and inference in machine learning models using distributed data while preserving privacy of the data owner. In the training mode, we deploy an encoder on the end-user device which extracts high level features from input data.
Yitian Zhang +4 more
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Distribution approximation in deep learning
2023Various machine learning problems are heavily related to distribution approximation. For example, the uncertainty distribution that characterizes the model variation under perturbation is usually difficult to access and how to approximate that distribution is one of the most critical issues in uncertainty quantification.
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