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Been Kim
@_beenkim
Research Scientist at Google DeepMind, PhD from MIT. Make machines empower people.
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    I've never imagined myself to be giving a keynote at one of the most prestigious ML conferences: ICLR. I thought those speaker seats are reserved for non-Asian, non-female, non-immigrant person who did not grow up in public housing and who has no imposter syndrome. 1/n
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    I'm hiring an intern at Google brain team 2022! Email me if you are 1) Graduating in 2022 (preferred) or early 2023 2) Interested in interpretability, representation learning or studying fundamental principles of how NN perceives the world. Pleas read the whole thread--> 1/n
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    Congratulations to the amazing @DGukesh ❤️😍🥰
    Thanks a lot sir for all the support and encouragement😊🙏
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    Our paper on understanding AlphaZero♟️is now published at PNAS! pnas.org/doi/10.1073/pn… The paper "studies" AZ's internals and its behaviors in collaborations with @DeepMind and world chess champion @32gcfhkmm. What did we learn? 🧵
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    Better way to do interpretability:♟️Interpretability has been my passion for more than a decade. Most of time however, I was frustrated; many method don't seem to meet their promise, some even provably wrong*. I felt stuck in this impossible task.
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    🔥🔥Our small team in Seattle Google DeepMind is hiring! 🔥🔥If you are willing to move to/already in Seattle, has done significant work on human-machine communication / interpretability (from ML side) with a relevant PhD and great publication record, Join us. Apply here 👉👉
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    Life achievement unlocked: coauthoring paper with the founder of @DeepMind @demishassabis and world chess champion @32gcfhkmm and many other amazing folks at @DeepMind. We used high level human chess concepts to look deeper into what self-taught super-human chess player.
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    Many previous work of mine and others hinted ‘something fishy’ about saliency-based methods. But we never had a rigorous proof of what we saw. This work “Impossibility Theorems for Feature Attribution", now published in PNAS, to me marks a point of new beginnings.
    Excited to finally share that "Impossibility Theorems for Feature Attribution" is published in PNAS. TL;DR Methods like SHAP and IG can provably fail to beat random guessing. w/ @natashajaques @PangWeiKoh @_beenkim PNAS: pnas.org/doi/10.1073/pn… arXiv: arxiv.org/abs/2212.11870
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    Feature visualizations are widely used interpretability tools - but can we trust them? We investigate this question from an adversarial 🥷, empirical 🔬 and theoretical 📝 perspective. The result: Don’t trust your eyes! (1/6) Paper: arxiv.org/abs/2306.04719 🧵
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    Being a Senior PC for one of the biggest conferences #ICLR2023 so far offered me 1) so many opportunities to make mistakes no matter how much time and efforts I pour in 2) to feel responsible for everything that went sub-optimally even if they were out of my control, but..🧵
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    Congratulations @wellingmax and @dpkingma again for winning the first ever test of time award at ICLR! #ICLR2024 I had the honor of being the photographer:)
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    Now that concludes #ICLR2023 - the first major ML conference in Africa that I get to be part of as a senior PC - the job that I probably spent the *most* of my last year thinking about, losing sleep over and passed many other opportunities for. 1/3
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    Thanks everyone for waiting patiently! #iclr2023 Call for paper for ICLR 2023 is out! Full paper deadline: Sept 28 iclr.cc/Conferences/20…
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    Excited and honored to give keynote at #ecmlpkdd2020 tomorrow! First talk since I became a mom. So it might mostly contain my baby's pictures, and a little bit about inqterpretability.😊