Akshay Agrawal

@akshaykagrawal

PhD candidate at Stanford; formerly at Google Brain.

Bay Area, California
Vrijeme pridruživanja: veljača 2013.

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  1. Prikvačeni tweet
    28. lis 2019.

    CVXPY is now differentiable. Try our PyTorch and TensorFlow layers using our package, cvxpylayers: (& see our NeurIPS paper for details )

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  2. 30. pro 2019.

    The upshot: on average, jobs finish faster and the variance in runtime is smaller, substantially decreasing tail latency.

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  3. 30. pro 2019.

    A very cool application of mathematical optimization: Netflix uses CVXPY in production to assign containers to threads, decreasing interference & contention for shared resources like caches.

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  4. 24. pro 2019.

    The code for our examples is at

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  5. 24. pro 2019.

    One of our first applications of differentiating through CVXPY problems — we design control policies by learning the structure of convex programs. The paper has examples for autonomous driving, finance, & supply chain optimization.

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  6. proslijedio/la je Tweet
    18. pro 2019.

    It took me a while to recap bc some papers are complex. Key points: - Destructing black box: convergence, generalization, neural tangent kernel - New approaches: Bayesian & uncertainty, graph, convex optimization - Neuroscience & bio-inspired algos - ...

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  7. 28. lis 2019.
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  8. 28. lis 2019.

    This is joint work with , , Stephen Boyd, Steven Diamond, and

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