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Brenden Lake
@LakeBrenden
Associate Professor of Computer Science and Psychology @ Princeton. Posts are my views only
Manhattan, NY
Joined June 2018
Posts
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    Today in Nature, we show how a standard neural net, optimized for compositional skills, can mimic human systematic generalization (SG) in a head-to-head comparison. This is the capstone of a 5 year effort with Marco Baroni to make progress on SG. (1/8) nature.com/articles/s4158…
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    I'm joining Princeton as an Associate Professor of Computer Science and Psychology this fall! Princeton is ambitiously investing in AI and Natural & Artificial Minds, and I'm excited for my lab to contribute. Recruiting postdocs and Ph.D. students in CS and Psychology — join us!
    Nassau Hall. Photo credit to Debbie and John O'Boyle
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    We train a self-supervised net "through the eyes" of one baby across 2 years of development. At #NeurIPS2020, Emin Orhan shows how high-level visual representations emerge. Paper & pre-trained net github.com/eminorhan/baby… Poster Thurs Noon EST neurips.cc/virtual/2020/p…
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    Which is more incorrect, the original tweet or Twitter's added context?
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    I got tenure! It was fitting that I got to celebrate with the lab right after the news. Working together for the last 6.5 years has been a blast.
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    Our new lab for Human & Machine Intelligence is officially open at Princeton University! Consider applying for a PhD or Postdoc position, either through the depts. of Computer Science or Psychology. You can register interest on our new website lake-lab.github.io (1/2)
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    What makes people smarter than machines? Reading list for my NYU class "Advancing AI through Cognitive Science" has paired papers in AI and CogSci organized by topic, highlighting key ingredients for building machines that learn and think like people. brendenlake.github.io/AAI-site/
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    We train large-scale neural nets "through the eyes" of one baby across 2 years of development. New paper from Emin Orhan shows how high-level visual representations emerge from a subset of one baby's experience, through only self-supervised learning. arxiv.org/abs/2007.16189 (1/2)
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    Children use the mutual exclusivity bias to learn new words, while standard deep nets show the opposite bias, often hindering learning. @gandhikanishk introduces this challenge for ML at #NeurIPS2020. Paper proceedings.neurips.cc//paper_files/p… Poster Thu Noon EST neurips.cc/virtual/2020/p…
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    Children use the mutual exclusivity (ME) bias to learn new words, while standard neural nets show the opposite bias, hindering learning in common scenarios. New preprint from @gandhikanishk introduces ME as a challenge for neural networks arxiv.org/abs/1906.10197
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    While cleaning out my office this morning, I found my first complete drawing of Bayesian Program Learning for Omniglot. The one piece of paper I'll keep! (1/2) science.org/doi/10.1126/sc…
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    Now out in Nature Machine Intelligence, Emin Orhan shows how high-level visual representations are learnable from a child's proxy visual input, without strong inductive biases, and can be applied to a range of visual benchmarks. nature.com/articles/s4225…
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    Professor Gary Cottrell took me in as a summer intern in high school, and introduced me to neural networks and cognitive science. I loved it, and his lessons ended up shaping my education and career. It's only appropriate that Gary teaches the next generation Lake too.
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    There are still open desks in our new Human & Machine Intelligence lab at Princeton. Express your interest in joining us: lake-lab.github.io/apply/