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(38) Pancreatic Cancer: Advances in Science and Clinical Care - YouTube
This AACR Special Conference, to be held September 21-24, 2018, in Boston, will convene world-renowned experts covering the breadth of research on pancreatic cancer, from early detection and genetics to precision medicine and immunotherapy.
healthcare  cancer  pancreas  AACR  state-of-art 
september 2018 by PieroRivizzigno
✨How to train a neural coreference model— Neuralcoref 2
The last months have been quite intense at HuggingFace 🤗 with crazy usage growth 🚀 and everybody hard at work to keep up with it 🏇, but we finally managed to free some time and update our…
coref  coreference  nlp  sota  state-of-art  deep-learning 
march 2018 by nharbour
English NLP SOTA · magizbox/underthesea Wiki
GitHub is where people build software. More than 27 million people use GitHub to discover, fork, and contribute to over 80 million projects.
ner  state-of-art  sota  deep-sleep  nlp  named-entity-recognition 
march 2018 by nharbour
Information Processing: Mathematical Theory of Deep Neural Networks (Princeton workshop)
"Recently, long-past-due theoretical results have begun to emerge. These results, and those that will follow in their wake, will begin to shed light on the properties of large, adaptive, distributed learning architectures, and stand to revolutionize how computer science and neuroscience understand these systems."
hsu  scitariat  commentary  links  research  research-program  workshop  events  princeton  sanjeev-arora  deep-learning  machine-learning  ai  generalization  explanans  off-convex  nibble  frontier  speedometer  state-of-art  big-surf  announcement 
january 2018 by nhaliday
Frontiers | Can We Validate the Results of Twin Studies? A Census-Based Study on the Heritability of Educational Achievement | Genetics
As for most phenotypes, the amount of variance in educational achievement explained by SNPs is lower than the amount of additive genetic variance estimated in twin studies. Twin-based estimates may however be biased because of self-selection and differences in cognitive ability between twins and the rest of the population. Here we compare twin registry based estimates with a census-based heritability estimate, sampling from the same Dutch birth cohort population and using the same standardized measure for educational achievement. Including important covariates (i.e., sex, migration status, school denomination, SES, and group size), we analyzed 893,127 scores from primary school children from the years 2008–2014. For genetic inference, we used pedigree information to construct an additive genetic relationship matrix. Corrected for the covariates, this resulted in an estimate of 85%, which is even higher than based on twin studies using the same cohort and same measure. We therefore conclude that the genetic variance not tagged by SNPs is not an artifact of the twin method itself.
study  biodet  behavioral-gen  iq  psychometrics  psychology  cog-psych  twin-study  methodology  variance-components  state-of-art  🌞  developmental  age-generation  missing-heritability  biases  measurement  sampling-bias  sib-study 
december 2017 by nhaliday
Genome Editing
This collection of articles from the Nature Research journals provides an overview of current progress in developing targeted genome editing technologies. A selection of protocols for using and adapting these tools in your own lab is also included.
news  org:sci  org:nat  list  links  aggregator  chart  info-foraging  frontier  technology  CRISPR  biotech  🌞  survey  state-of-art  article  study  genetics  genomics  speedometer 
october 2017 by nhaliday
[1709.06560] Deep Reinforcement Learning that Matters
I’ve been experimenting w/ various kinds of value function approaches to RL lately, and its striking how primitive and bad things seem to be
At first I thought it was just that my code sucks, but then I played with the OpenAI baselines and nope, it’s the children that are wrong.
And now, what comes across my desk but this fantastic paper: (link: How long until the replication crisis hits AI?
Seriously I’m not blown away by the PhDs’ records over the last 30 years. I bet you’d get better payoff funding eccentrics and amateurs.
There are essentially zero fundamentally new ideas in AI, the papers are all grotesquely hyperparameter tuned, nobody knows why it works.

Deep Reinforcement Learning Doesn't Work Yet:
Once, on Facebook, I made the following claim.

Whenever someone asks me if reinforcement learning can solve their problem, I tell them it can’t. I think this is right at least 70% of the time.
papers  preprint  machine-learning  acm  frontier  speedometer  deep-learning  realness  replication  state-of-art  survey  reinforcement  multi  twitter  social  discussion  techtariat  ai  nibble  org:mat  unaffiliated  ratty  acmtariat  liner-notes  critique  sample-complexity  cost-benefit  todo 
september 2017 by nhaliday
Accurate Genomic Prediction Of Human Height | bioRxiv
Stephen Hsu's compressed sensing application paper

We construct genomic predictors for heritable and extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, ~40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate ~0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction.
I'm in Mountain View to give a talk at 23andMe. Their latest funding round was $250M on a (reported) valuation of $1.5B. If I just add up the Crunchbase numbers it looks like almost half a billion invested at this point...

Slides: Genomic Prediction of Complex Traits

Here's how people + robots handle your spit sample to produce a SNP genotype:
study  bio  preprint  GWAS  state-of-art  embodied  genetics  genomics  compressed-sensing  high-dimension  machine-learning  missing-heritability  hsu  scitariat  education  🌞  frontier  britain  regression  data  visualization  correlation  phase-transition  multi  commentary  summary  pdf  slides  brands  skunkworks  hard-tech  presentation  talks  methodology  intricacy  bioinformatics  scaling-up  stat-power  sparsity  norms  nibble  speedometer  stats  linear-models  2017  biodet 
september 2017 by nhaliday

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