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[TLDR] Honorable Mention: Zhengqi Li @Cornell University & Google Research
August 2019

"This paper examines 3D reconstruction of scenes with people from monocular video. The paper shows insightful creation of a dataset for this task, in addition to a solid execution of a state-of-the-art algorithm," the Award Committee stated.  "It has strong potential to be impactful paper in this area, facilitating future work in outdoor reconstruction with complex moving scenes."

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[Tips] Top 5 Work From Home Tips for Researchers and Scientists
March 2020

As the world continues to work from home, there are some positions that may have a harder time transitioning away from their usual workplace. Researchers and scientists have found themselves unable to access their labs or discuss with their colleagues in person. Here, we offer a guideline of 5 tips to help make the adjustments easier.  
1. Create a Productive Routine
2. Organize and Analyze Your Data
3. Work on Research Papers
4. Practice Self-Care
5. Work on Personal Development

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[Q&A] w/ Martin Schrimpf @ MIT
December 2019

Q: Would you like to share with our community about the paper that you got accepted about the recurrent networks?

A: The overarching idea that we're looking at is that there's an increased opportunity again for machine learning and neuroscience to communicate and present two advances in that direction. So first we are presenting Brain-Score, which is a platform that allows you to compare models with the brain and specifically models of vision with the ventral stream. The platform, given the model, will tell you how closely matched you are to the brain under various measurements. Like do the model and brain internal, the neurons, do they match up? And do the models, and brain externals, the behavior, also match up. And then second we present CORnet-S, which is a model that we recently developed, and which we think is more brain-like. Specifically, this model transforms deep networks into much more shallow networks. And it still maintains high performance by the virtue of having recurrence.

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