Search Results - Jajal, Purvish

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  1. 1
    Fractal dimension of retinal vasculature as an image quality metric for automated fundus image analysis systems

    Fractal dimension of retinal vasculature as an image quality metric for automated fundus image analysis systems by Lyu, Xingzheng, Jajal, Purvish, Tahir, Muhammad Zeeshan, Zhang, Sanyuan

    Published in Scientific reports
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  2. 2
    Simulation-Driven Design of Wood Framing Support Systems for Off-Site Construction Machinery

    Simulation-Driven Design of Wood Framing Support Systems for Off-Site Construction Machinery by Martinez, Pablo, Livojevic, Michelle, Jajal, Purvish, Aldrich, Daniel Ryan, Al-Hussein, Mohamed, Ahmad, Rafiq

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    An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions

    An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions by Tung, Caleb, Eliopoulos, Nicholas, Jajal, Purvish, Ramshankar, Gowri, Yang, Cheng-Yun, Synovic, Nicholas, Zhang, Xuecen, Chaudhary, Vipin, Thiruvathukal, George K., Lu, Yung-Hsiang

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  4. 4
    Pruning One More Token is Enough: Leveraging Latency-Workload Non-Linearities for Vision Transformers on the Edge

    Pruning One More Token is Enough: Leveraging Latency-Workload Non-Linearities for Vision Transformers on the Edge by Eliopoulos, Nick John, Jajal, Purvish, Davis, James C, Liu, Gaowen, Thiravathukal, George K, Lu, Yung-Hsiang

    Published in arXiv.org
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  5. 5
    Reusing Deep Learning Models: Challenges and Directions in Software Engineering

    Reusing Deep Learning Models: Challenges and Directions in Software Engineering by Davis, James C, Jajal, Purvish, Jiang, Wenxin, Schorlemmer, Taylor R, Synovic, Nicholas, Thiruvathukal, George K

    Published in arXiv.org
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  6. 6
    Token Turing Machines are Efficient Vision Models

    Token Turing Machines are Efficient Vision Models by Jajal, Purvish, Eliopoulos, Nick John, Chou, Benjamin Shiue-Hal, Thiravathukal, George K, Davis, James C, Lu, Yung-Hsiang

    Published in arXiv.org
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  7. 7
    Analysis of Failures and Risks in Deep Learning Model Converters: A Case Study in the ONNX Ecosystem

    Analysis of Failures and Risks in Deep Learning Model Converters: A Case Study in the ONNX Ecosystem by Jajal, Purvish, Jiang, Wenxin, Tewari, Arav, Kocinare, Erik, Woo, Joseph, Sarraf, Anusha, Lu, Yung-Hsiang, Thiruvathukal, George K, Davis, James C

    Published in arXiv.org
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  8. 8
    PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages

    PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages by Jiang, Wenxin, Synovic, Nicholas, Jajal, Purvish, Schorlemmer, Taylor R, Tewari, Arav, Pareek, Bhavesh, Thiruvathukal, George K, Davis, James C

    Published in arXiv.org
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  9. 9
    An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions

    An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions by Tung, Caleb, Eliopoulos, Nicholas, Jajal, Purvish, Ramshankar, Gowri, Chen-Yun, Yang, Synovic, Nicholas, Zhang, Xuecen, Chaudhary, Vipin, Thiruvathukal, George K, Lu, Yung-Hsiang

    Published in arXiv.org
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