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Prikvačeni tweet
Now out: my wife's fantastic book on solar power finance!https://twitter.com/worldscientific/status/1148579428185845760 …
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I can confirm: She rates "Where is Mr. Penguin" much more highly.https://twitter.com/solar_chase/status/1173257933439868929 …
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The video abstract of "Solar Power Finance without the Jargon":https://twitter.com/worldscientific/status/1152022173952798722 …
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Fascinating and plausible - you wonder though whether can distinguish higher numbers, but just don't see a point for it while on the hunt... What freaks me out more than all those spiders is that the article is bizarrely null hypothesis testing obsessed.https://twitter.com/ylecun/status/1144589697160818689 …
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Exciting! As a test-reader during the writing, I can say that it's a great read. Declaration of conflicts of interest: married to author.https://twitter.com/solar_chase/status/1141329570047635464 …
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Björn Holzhauer proslijedio/la je Tweet
EfficientNets: a family of more efficient & accurate image classification models. Found by architecture search and scaled up by one weird trick. Link: https://arxiv.org/abs/1905.11946 Github: https://bit.ly/30UojnC Blog: https://bit.ly/2JKY3qt pic.twitter.com/RIwvhCBA8x
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Björn Holzhauer proslijedio/la je Tweet
I’m honoured and excited to be keynoting PyCode Conference this year!https://twitter.com/pycode_conf/status/1134046461895876608 …
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Predictable result of our toddler wanting to hug the goslings.pic.twitter.com/YyRiTnBBDX
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What also worked well: a single random effects model with an extra parameter that lets historical data differ from new data. That parameter gets a prior that approximates model averaging between borrowing & no borrowing of information (spiky near 0, but with long tails).
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I looked at mixtures of a vague + an informative (based on historical data) joint prior for the mean & SD of random trial main effects. Weights <=0.5 (and probably lower) for the informative prior component looked good in my simulations.
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The accepted version of my paper on this was just posted at the Statistics in Biopharmaceutical Research website. I focused on first events/patient-year data, but I think findings should generalize to other data types. Let me know, if you want to know more/the full article.
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How to use prior information from historical trials in Bayesian meta-analyses? How to make them robust to prior-data conflicts (old vs. new controls)? How much weight to give the historical prior information?https://doi.org/10.1080/19466315.2019.1610043 …
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We had hoped the goose eggs would hatch today. Instead: 11 ducklings in our garden that a duck hatched in the reeds of our pond. Since then she's made them jump off a 2.5m wall, ended up on the wrong side of our 2m electrified fence (we rescued them) and barged in on our dinner.pic.twitter.com/9eOhbS5g9Z
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Björn Holzhauer proslijedio/la je Tweet
What a time to be alive: collecting whale mucus via UAV! No doubt the first stage in a complex pipeline for data analysis or machine learning. Anyone else have equally exotic data collection techniques in work they're doing?https://twitter.com/neurosocialself/status/1114000960718659584 …
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Björn Holzhauer proslijedio/la je Tweet
Today I took the day off work to go through the edited proofs of my book, Solar Power Finance without the Jargon. Really wish I hadn't got so many 2017 numbers to update to 2018. Also I had to log into work to tell my clients about SolarReserve Aurora being cancelled.
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Björn Holzhauer proslijedio/la je Tweet
#Temperature anomalies 1880-2017 by country
. No matter how you visualize it, it looks scary! #GISTEMP#dataviz#climatechange#globalwarming Download / watch hi-res
: https://flic.kr/p/293M1oa pic.twitter.com/cAn9wG8FPUHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Björn Holzhauer proslijedio/la je Tweet
ULMFit from
@fastai + Data Augmentation with backtranslation can get 80+% validation accuracy using only 50 training examples on#NLP IMDB sentiment classification! Full paper for#cs224n at https://arxiv.org/abs/1903.09244 . Thread below.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Björn Holzhauer proslijedio/la je Tweet
Work by pharma statisticians https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.12242 … shows that a huge number of historical patients can easily be beaten by a few concurrent controls. The problem is commentators who don’t understand the stats.
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Björn Holzhauer proslijedio/la je Tweet
state-of-the-art in
#AI,#MachineLearning - 530 leaderboards • 974 tasks • 712 datasets • 9205 papers with code - Computer Vision - Natural Language Processing - Medical - Speech and morehttps://buff.ly/2RxMgevHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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| Feb 2019