Aude Genevay
@a_gnv
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postdoc researcher in ML @MIT.
Joined April 2011
Naive question: why are people excited about normalizing flows/neural ODEs? Aren't they essentially a rehash of GANs that's even harder to train? I've been looking at many papers and still can't figure out what justifies their popularity, so would love some enlightened input!
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Trying to convince myself that I don't care about #NeurIPS2020 decisions but I have already checked CMT at least 5 times today 'just in case' so I guess I'll stop lying to myself.
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I was having a great saturday morning watching the magnificent Pyrénées on #TourdeFrance until Thibaut Pinot started falling behind in Port de Balès. So I'm going back to bed in total denial.
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New paper with my colleagues at MIT, kudos to PhD student Lingxiao Li for an impressive first paper in computational OT!
Wasserstein barycenters are useful in stats/ML but typical algorithms discretize the domain, leading to low-dimensional/coarse approximations. With PhD student Lingxiao Li, postdoc Aude Genevay @a_gnv, and @MITIBMLab's Misha Yurochkin we construct a continuous barycenter!🔥🎊🎈🎉
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Wasserstein barycenters are useful in stats/ML but typical algorithms discretize the domain, leading to low-dimensional/coarse approximations. With PhD student Lingxiao Li, postdoc Aude Genevay @a_gnv, and @MITIBMLab's Misha Yurochkin we construct a continuous barycenter!🔥🎊🎈🎉
Continuous Regularized Wasserstein Barycenters. (arXiv:2008.12534v1 [cs.LG])
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Positions confirmed! Still hoping to hear from more great candidates at the intersection of ML and geometric data. Please send me a quick email if you’re interested or have questions! And, share with any interested colleagues or students.
Our group in @MIT_CSAIL will likely (finalizing funding) have an opening for a postdoc at the intersection of ML and geometric representations (eg meshes, CAD, LiDAR) to start late 2020/early 2021. Check out our web page to get an idea of what we’re up to: https://t.co/HE4i5v8oa6
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"We were told we couldn't do math because we were women." Karen Uhlenbeck proved her peers wrong. And she has since contributed to paving the way for women in STEM. That's worth celebrating this International Women's day. #WomenInStem #womensday #Marcheighth #AbelPrize
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“In fairness, this lack of diversity is typical in AI.” stop excusing companies for lack of diversity. These hand-wringing ‘so hard to recruit and retain diverse talent’ and ‘we aren’t worse than google’ are such garbage excuses for maintaining the status quo.
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