TrAdE toDaY

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Right, screw it. Here's a thread on *that* FBLive by Alex Cairnie about #Govanhill. Lets do a watch-through shall we? (Mind, I'm a resident of Govanhill, and a sociologist. I'm also 5'3, and a visibly queer woman so if half his claims are true I should be living in terror)

00:30: Welcomes viewers and uses points out that his videos are only getting attention b/c they're 'horrific'. Directs folk to his Youtube page. So already clout hunting.

1:08: "I've been warned to stay out of Govanhill. I'm a marked man" (proceeds to use a beanie as a disguise)

1:30 "Laughing and joking aside, here's somebodys front garden." Proceeds to show litter and claim its thrown from windows and this is 'common'. I know a handful of streets with litter issues like this, but its neither common nor unique to #govanhill and other deprived areas.

2:10 "Don't be under any illusion I'm only showing you the bad bits" - only shows you the bad bits.

3:45: He's had offers of help, but hes said no because hes not responsible for 'anybody elses safety' Good lord.

5:01: "Its not that bad. But its not all good either" -Correct

6:00: TW: child abuse - He starts claiming to have been contacted by a health professional and told about child abuse 'in the area'. Huge ethical issues here as the 'professional' who has 'been in the game a long time' is reporting to Facebook Dude and not authorities.

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.

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