SigOpt

@SigOpt

SigOpt is a standardized, scalable, enterprise-grade optimization platform and designed to unlock the potential of your modeling pipelines.

San Francisco, CA
Vrijeme pridruživanja: rujan 2014.

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  1. prije 16 sati

    A great summary of the concepts behind from , for those ready to move on from random, manual, or grid search:

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  2. 2. velj

    Join us later this week at ! On February 7th, catch ' presentation on rapid high-resolution approximation of a patient's psychometric function at the Health Intelligence Workshop:

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  3. 1. velj

    Interested in training more accurate models? We're hiring for Specialists, Interns, a Lead, and more. Apply now:

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  4. 30. sij

    "Tuning the Un-Tunable" – shares recommendations for tuning lengthy and expensive models more efficiently:

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  5. 29. sij

    Tonight in SF: SigOpt's Head of Product Fay Kallel is on a panel – "How to Manage Products" along with , Bastiane Huang , Dave Anderson , and more. Get tickets:

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  6. 28. sij

    We'll be at this year. Join us February 7th for a presentation by on his research and February 11th at the AI Job Fair. Details here:

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  7. 27. sij

    On Wednesday, check out SigOpt's Head of Product Fay Kallel on a panel – "How to Manage Products by Product Leaders." Get free advance tickets here:

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  8. 26. sij

    To show how SigOpt works with any kind of data and any kind of model, did a live demo with random data in front of of . Watch it here:

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  9. 24. sij

    One of our other favorite papers from was by , a former SigOpt research intern, developing a strategy to certify a neural network as robust to a variety of input transformations. Learn more here:

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  10. 23. sij

    Need a quick refresher on Bayes' Theorem? Check out this great animated visual of the concept by :

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  11. 22. sij

    How can we build the best models with long training times? shares a process involving transfer learning that cuts down on both time and costs in this talk:

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  12. 21. sij

    Check out some of our favorite highlights from , including a paper on Cost Effective Active Search by , , and Benjamin Moseley exploring intelligent sampling for a predefined number of positive observations:

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  13. 20. sij

    Did you know SigOpt has a rich documentation section on our website? Get your experiments up and running faster with our quickstart guides, how-to's, and full API reference:

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  14. 19. sij

    Looking for some / inspiration in the new year? Check out this list of the top 10 YouTube channels, from 's Two Minute Papers to 's concept visualizations:

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  15. 17. sij

    How might we design better glass for that doesn't lose as much energy to reflection? Learn more about our ongoing collaboration with scientists from :

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  16. 16. sij

    Congratulations to on their release of Optuna v1.0: Great work! Looking forward to presenting with you at the Optimization 2020 meeting in May.

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  17. 16. sij

    "Generalization is at the core of machine learning. Learned models are only useful if they can generalize to unseen data from a finite set of examples." Learn more about Neal Brady's work on redefining the bias-variance tradeoff in our Q&A:

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  18. 15. sij

    Your textbook needs an update: Neal Brady's research shows that both bias and variance decrease with network width. Read the paper to new perspective on the bias-variance tradeoff:

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  19. 14. sij

    An update on our collaboration with , applying to understand the tradeoffs between nanowires and nanocones in the structure of glass for :

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  20. 13. sij

    "It is quite surprising/refreshing to see a paper with ‘Least-Mean-Squares Solvers’ in its title at NeurIPS." Check out the recap of some of our favorite papers at this year!

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