Claudio Michaelis

@clmich

building polybot, a flexible robot for a sustainable agriculture future

Tübingen, Deutschland

Joined April 2010

629 Following

398 Followers

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Claudio Michaelis
@clmich
Nov 7, 2022
Blows my mind that our POLYBOT made it into the opening of #COP27 🤯
Wieland Brendel
@wielandbr
Nov 7, 2022
Yeah! Our work on an intelligent #robot for #sustainable #farming (POLYBOT) made it into the opening of #COP27 World Leader Summit in the talk of @VeronikaEyring. Interested in bold climate tech solutions? Talk to me or @clmich at #COP27 or DM us.@Cyber_Valley @MPI_IS @ml4science
Claudio Michaelis
@clmich
May 30, 2022
Come work with us on #robotics for #sustainable agriculture! 🤖🌱
Wieland Brendel
@wielandbr
May 30, 2022
Just started a group at Max Planck Institute for Intelligent Systems (@MPI_IS) & Tübingen AI Center🎉 Focus: robustness, identifiable representation learning & interpretability. Plus a cool project on #AI, #robotics & #sustainable food production. Interested in a #postdoc? DM me!
Claudio Michaelis
@clmich
Nov 10, 2020
More classes is all you need for one-shot object detection. It turns out we can almost close the gap between the detection of known and novel objects simply by increasing the number of categories. arxiv.org/abs/2011.04267 @clmich @alxecker @MatthiasBethge [1/7]
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Claudio Michaelis
@clmich
Aug 10, 2020
Nice thread explaining that the "double-descend" phenomenon that riddles DL researchers can be observed for something as simple as cubic splines
Daniela Witten
@daniela_witten
Aug 9, 2020
The Bias-Variance Trade-Off & "DOUBLE DESCENT" 🧵 Remember the bias-variance trade-off? It says that models perform well for an "intermediate level of flexibility". You've seen the picture of the U-shape test error curve. We try to hit the "sweet spot" of flexibility. 1/🧵
Claudio Michaelis
@clmich
Feb 14, 2020
Unsupervises representations are here to stay! Love the transfer learning analysis in this work!
Ting Chen
@tingchenai
Feb 14, 2020
Introducing SimCLR: a Simple framework for Contrastive Learning of Representations. SimCLR advances previous SOTA in self-supervised and semi-supervised learning on ImageNet by 7-10% (see next). arxiv.org/abs/2002.05709 Joint work with @skornblith @mo_norouzi @geoffreyhinton.

Claudio Michaelis

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