Yet another nice book (undergraduate prerequisites only). The last chapter is the culmination of this book: It explains manifolds arising from group actions.
link.springer.com/book/10.1007/9…
Can NNs learn and enforce symmetries on their predictions?
Inspired by Noether’s Thm, we do so by learning quantities that should be conserved.
This method can recover known conservation laws in scientific data & can be applied to raw video data.
Paper: lis.csail.mit.edu/noether
This was a serendipitous finding while working on a much larger project on uncertainty in control. Was rather surprising to find that such a simple rule can be equivalent to optimizing a free energy functional
This was a serendipitous finding while working on a much larger project on uncertainty in control. Was rather surprising to find that such a simple rule can be equivalent to optimizing a free energy functional
Our latest paper was published today. It’s about the classic (and amazing!) phenomenon of pendulum clocks that get in sync. Metronomes can do something similar, as you may have seen in videos on YouTube. Hopefully the intro of the paper is readable. Enjoy! aip.scitation.org/doi/10.1063/5.…
Today, @iddo and me are presenting a tutorial on meta-learning at #AAAI2021. For those who can't make it, we made all materials (slides and videos) publicly available here: sites.google.com/mit.edu/aaai20… Happy to (try to) answer any questions you may have :). #AI#MachineLearning
This is one of my favorite videos in the sparsity series: Sparse Regression with the LASSO
We use LASSO all the time to discover interpretable models that prevent overfitting (like SINDy)
Check out the YouTube video!
youtu.be/GaXfqoLR_yI
We’ve partnered with @VirtualFly1 to give away their High-End flight controls + a Premium Deluxe copy of MSFS!
Starting off with their awesome Yoko+ ‘The Yoke’! To enter:
1. Like us + @VirtualFly1
2. RT
Winner will be announced Dec. 1st
#MicrosoftFlightSimulator#virtualfly
i learned about these from the wonderful paper of curto, degeratu, itskov, & morrison. this uses a piecewise-linear model for each neuron. everything is driven by the network interactions -- whether strongly or weakly inhibitory.
2/
arxiv.org/abs/1605.04463
We discover that the prior knowledge embedded in the physics computation itself acts as an implicit regularization that greatly improves generalization of machine learning models for physics.
Please check out our recent paper: arxiv.org/abs/2009.08551
📢 NVAE source code is released!
NVAE is deep hierarchical VAE w/ specially designed network architecture that can generate high-quality images & achieve SOTA log-likelihood.
Happy coding!
paper: arxiv.org/abs/2007.03898
code: github.com/NVlabs/NVAE
w/ @jankautz at @NVIDIAAI
IT'S FINALLY OUT!
After months of work, I'm happy to announce that the first video in a series on visualizing deep learning is out. It's on visualizing the structure of a neural network.
RTs appreciated :)
youtu.be/UOvPeC8WOt8
Robert Bosch has created a fascinating series of instances of the traveling salesman problem (TSP) that provide continuous-line drawings of well-known pieces of art. Source: bit.ly/2Cq3Pfh
If you put some soap or ink on a leaf, you obtain a ink propelled boat, using the Marangoni effect. The ink or the detergent has a lower surface tension than water and its flow provides thrust to push the boat forward buff.ly/2tnyzW6 [gif: buff.ly/2X23epo]
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