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- Chien Chern CheahSuggested from co-authorship
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Works16 from public data
- A Layer-Wise Theoretical Framework for Deep Learning of Convolutional Neural Networks86
This paper aims to provide a theoretical methodology to investigate and train deep convolutional neural networks so as to ensure convergence, and shows a reasonable trade-off between accuracy and analytic learning.
- A Theoretical Framework for End-to-End Learning of Deep Neural Networks With Applications to Robotics13
This paper presents the first unified End-to-End (E2E) learning framework that can be applied to both classification problems and real-time kinematic robot control tasks and shows that convergence can be ensured during training.
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- An Analytic End-to-End Deep Learning Algorithm based on Collaborative Learning1
A convergence analysis for end-to-end deep learning of fully connected neural networks (FNN) with smooth activation functions based on non-smooth ReLU activation functions is presented, which avoids any potential chattering problem and does not easily lead to gradient vanishing problems.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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