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Oldies but goldies: G. H. Golub, Christian Reinsch, Singular value decomposition and least squares solutions, 1970. The most popular algorithm to compute efficiently the SVD decomposition. https://en.wikipedia.org/wiki/Singular_value_decomposition …pic.twitter.com/esxUfkpbiZ
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Reevaluating the Role of Persistent Neural Activity in Short-Term Memory, https://bit.ly/2S0mfXC by Nicolas Masse, Matthew Rosen, & David Freedmanpic.twitter.com/3KTFuZJ6Yj
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Just came across
@ch402’s great blog and really enjoyed reading one of his posts on long short-term memory (LSTM): https://colah.github.io/posts/2015-08-Understanding-LSTMs/ … Made me intereted in learning more about#ML.#weekendreadsHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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"So I have decided to stop attempting to generate new mathematics, and concentrate instead on carefully checking “known” mathematics on a computer." http://www.andrew.cmu.edu/user/avigad/meetings/fomm2020/slides/fomm_buzzard.pdf …
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A practical definition of opportunity cost: If you spend too much time working on good things, then you don’t have much time left to work on great things. Understanding opportunity cost means eliminating good uses of time. And that's what makes it hard.
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+, Σ, and ∫ are just different evolutions of the same Pokémon
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I went and wrote an introduction on Bayesian Neural Networks based on a lecture by David MacKay. Included are examples in JAX/Python. Give us a click so we can finally hit some of our OKRs for this quarter :Phttps://engineering.papercup.com/posts/bayesian-neural-nets/ …
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Looking for a post-doc. The research topics can be very broad including graph representation learning, graph neural networks, drug discovery, knowledge graphs, deep generative models, and natural language understanding. Pls email me if you are interested.
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How symmetric is too symmetric for large quantum speedups? https://ift.tt/311FcNz
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Heya
Want to get research experience at CMU?
If you're interested in PL, distributed systems, software engineering, etc we have a program that pays you to come learn to do research with us over the summer!
https://www.cmu.edu/scs/isr/reuse/
Happy to answer Qs about it!
RTs welcome
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You know how you sometimes grab a travel guide to a place you've lived in a while and know well, and you read a confident and savvy-sounding description of something that's utterly unrecognizable to you? That's how I feel about most tech journalism.
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My blogpost on how & why we use convolutional neural networks as a model of the visual system is probably the most read thing I've ever written and it's now been expanded & updated into a proper review article, complete with 136 references & 5 new figures! https://arxiv.org/abs/2001.07092 pic.twitter.com/xRqYKptTsF
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2019
was quite the year for Deep Reinforcement Learning. In todays blog post I list my top 10 papers 

https://roberttlange.github.io/posts/2019/12/blog-post-9/ … What was your favourite paper? Let me know!pic.twitter.com/CqD4PqjEKV
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Norge har i dag fått en nasjonal strategi for kunstig intelligens. Gratulerer! Veldig glad for at regjeringen vil styrke grunnleggende IKT-forskning og satse på utdanning! Takk
@nikolaiastrup! https://www.regjeringen.no/no/dokumenter/nasjonal-strategi-for-kunstig-intelligens/id2685594/ …@NORAdotAI@UiB@II_UiB@UiBmatnat@infomedia_uib@RektoratUiBHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Under-rated question for better planning and prioritization: Will this problem go away if I do nothing? More frequently than you suspect, it will.
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Seeing twitter debate about the replicability of the new DeepMind study of ML for breast cancer screening https://www.nature.com/articles/s41586-019-1799-6 … vs. the NYU study from October: https://medium.com/@jasonphang/deep-neural-networks-improve-radiologists-performance-in-breast-cancer-screening-565eb2bd3c9f … Did some light digging in to compare/contrast… 1/n
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Bayesian methods are *especially* compelling for deep neural networks. The key distinguishing property of a Bayesian approach is marginalization instead of optimization, not the prior, or Bayes rule. This difference will be greatest for underspecified models like DNNs. 1/18https://twitter.com/carlesgelada/status/1208618401729568768 …
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Some people in ML share the illusion that models expressed symbolically will necessarily/magically generalise better compared to, for example, parametric model families fit on the same data. This belief seems to come from a naive understanding of mathematics 1/5
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Vis a vis the Marcus / Bengio
#AIDebate, I just don't buy the implied strict separation between "System 1" and "System 2".Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Did Marcus quote a 90s paper just to flex on Bengio
? #AIDebateHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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