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Creators/Authors contains: "Yu, Stella X"

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  1. ; ; ; (, International Conference on Machine Learning)
    Free, publicly-accessible full text available July 13, 2026
  2. ; (, IEEE/CVF Conference on Computer Vision and Pattern Recognition)
    Free, publicly-accessible full text available June 11, 2026
  3. ; ; ; ; (, International Conference on Learning Representations)
    Free, publicly-accessible full text available April 24, 2026
  4. ; ; (, European Conference on Computer Vision)
    Accepted Manuscript Full Text Available
  5. ; ; ; (, IEEE/CVF Conference on Computer Vision and Pattern Recognition)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; (, IEEE/CVF CVPR Workshop on Populating Empty Cities: Virtual Humans for Robotics and Autonomous Driving)
    Accepted Manuscript Full Text Available
  7. ; ; (, International Conference on Learning Representations)
    Accepted Manuscript Full Text Available
  8. ; ; ; (, International Conference on Computer Vision)
    Accepted Manuscript Full Text Available
  9. ; ; ; (, IEEE)
    Full Text Available 
  10. ; ; ; ; ; ; (, arXivorg)
    How much can you say about the gradient of a neural network without computing a loss or knowing the label? This may sound like a strange question: surely the answer is “very little.” However, in this paper, we show that gradients are more structured than previously thought. Gradients lie in a predictable low-dimensional subspace which depends on the network architecture and incoming features. Exploiting this structure can significantly improve gradient-free optimization schemes based on directional derivatives, which have struggled to scale beyond small networks trained on toy datasets. We study how to narrow the gap in optimization performance between methods that calculate exact gradients and those that use directional derivatives. Furthermore, we highlight new challenges in overcoming the large gap between optimizing with exact gradients and guessing the gradients. 
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    Accepted Manuscript Full Text Available