“We don’t negotiate salaries” is a negotiation tactic.

Always. No, your company is not an exception.

On December 25 Mr Pool retweeted August 16 message about the Phoenix and that the worst was still on the horizon to come. 1/*

Maybe now we can assume that November 3 message meant "FUD" nor "FUN". Putting the word in capital letters seems to indicate the aspect the word fud has in our space, FUD. 2/*

Brad started it on December 22 (1/3 posts (Don't say? Sure) = 13 = Plan B). 3/*

And immediately the SEC followed dropping the bomb (1.3 billions (Don't say? Sure) = 13 = Plan B). 4/*

FUD materializes, Bitstamp is one of the first that haltes XRP trading and so on. 5/*

*Heads-up that this thread will feature some extreme examples of homophobia and transphobia.*

Hey folks, have you ever wondered why trans people face constant accusations of fetishism, sexual predation and child abuse/grooming? Well, today let's talk about stochastic terrorism.

So, first we have to talk about what stochastic terrorism actually is. Remember when there was a conspiracy that there was a pedophile ring operating out of a pizza parlour basement, and a crazed gunman showed up? It's that sort of incitement that stochastic terrorism describes.

It's the demonisation or incitement against a group of people or individuals with the intent that other, unaffiliated parties will act upon it; it's the releasing doctored footage and a shooter show up at Planned Parenthood as a direct result of what he's been led to believe.

So with that in mind, let's talk about Russia's extreme anti-LGBT laws that banned the "promotion" of LGBT lifestyles to children. A move that specifically marked LGBT people as a threat to children, and resulted in neo-nazis meting out vigilante "justice" against gay men.

Groups such as this would lure unsuspecting gay men via dating sites, and brutalise and humiliate them on camera. They did so under the guise of combating child abusers, one group calling themselves "Occupy

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.