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Showing 1 to 5 of 5 for “"neural architecture design"”.

  1. NEURAL ARCHITECTURE DESIGN AND APPLICATIONS

    Neural architecture design is crucial in AI development. This thesis first examines the macroscopic architecture of Transformers, challenging the belief that their attention-based token mixer is key. By replacing the attention module with a simple spatial pooling operator, we create PoolFormer, …

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  2. Optimization and automation for efficient neural architecture design

    Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms

    uiuc Repository record for Optimization and automation for efficient neural architecture design (opens in a new tab)

  3. Demystifying deep network architectures : from theory to applications

    Deep neural networks significantly power the success of machine learning and artificial intelligence. Over the past decade, the community keeps designing architectures of deep layers and complicated connections. Many works in deep learning theory tried to understand deep networks from different …

    texas Repository record for Demystifying deep network architectures : from theory to applications (opens in a new tab)

  4. Designing Highly-Efficient Hardware Accelerators for Robust and Automatic Deep Learning Technologies

    … AI technologies, such as deep convolutional neural networks (DNNs), have recently achieved amazing success in numerous applications, such as image recognition, autonomous driving, and so on. However, there are two critical issues in the conventional DNN applications. The first problem is …

    houston Repository record for Designing Highly-Efficient Hardware Accelerators for Robust and Automatic Deep Learning Technologies (opens in a new tab)

  5. Practical processing and acceleration of graph neural networks

    … Machine Learning (ML) in the past decade. Deep neural networks have achieved, or surpassed, human-level on diverse tasks ranging from image classification to game playing. In these applications, we typically observe that the input to the model has some form of regular structure: for example, …

    cambridge Repository record for Practical processing and acceleration of graph neural networks (opens in a new tab)