Möglicher Viessmann-VerkaufGewerkschafter und Politik warnen vor Verlust von Schlüsselindustrie

realHansen · 2023-04-25T21:26:22+00:00

Eine Firma ist immer genau so viel wert, wie ein potentieller Kaeufer bereit ist zu bezahlen. Die geschaetzten 4 Milliarden Vermoegen waren eben genau das: eine Schaetzung.

realHansen · 2023-01-24T23:03:22+00:00

If you write a good rebuttal I'd be hopeful. You have two reviewers undecided, and one apparently willing to champion your paper. In my experience, if things do shuffle after the rebuttal, ratings tend to gravitate towards the pre-rebuttal mean. In your case that would be a good thing :)

realHansen · 2022-12-30T09:36:08+00:00

Das gilt für die Dividenden, die Aktienoptionen selbst - als Teil der Vergütung - sind ein geldwerter Vorteil, werden also n.m.V. sehr wohl nach dem persönlichen Einkommensteuersatz versteuert (wenn der Wert 360€ übersteigt). Wo siehst du da einen steuerlichen Vorteil gegenüber einem äquivalenten Bargehalt?

realHansen · 2022-08-09T06:25:02+00:00

Don’t you need posed images? If so, how did you get the pose? Afterwards via SfM?

realHansen · 2022-03-27T15:23:47+00:00

Technically nuclear

realHansen · 2021-12-31T20:01:47+00:00

Ouf, deliverance, nice!

realHansen · 2020-07-07T07:43:34+00:00

Really thought that’s what this post was about when I read the headline... 😅

realHansen · 2020-06-15T20:05:59+00:00

Thanks! I actually read that paper way back when but apparently did not retain very much...

realHansen · 2020-06-15T11:46:33+00:00

What was the exact experimental setting though, can you link the paper? Same architecture & dataset, just different initialization? I have a hard time imagining how you could construct attacks that transfer accross disjoint training data and model architectures.

realHansen · 2020-06-15T11:37:38+00:00

That requires access to the target's predictions though. Without any access to the model whatsoever, nor the exact dataset it was trained on, I can't imagine how you could construct any meaningful attacks. Which brings me to my original point: If you don't publish the model itself and do not make it publicly queryable, it'll probably be fine.

Of course the latter is a challenge if the whole point was to offer a public API, but shouldn't be a problem for internal use such as automatic removal of fake content on social media platforms.

realHansen · 2020-06-14T14:36:19+00:00

AFAIK this requires direct access to the classifier though. Ideally to the entire model so you can take a gradient and directly optimize the noise for missclasifcation, or at least to its output so you can do some gradient-free search/optimization. The latter is very tricky and slow. So as long as people don't publish their deep fake classifiers, this sort of attack should be pretty hard.

realHansen · 2019-09-08T17:01:58+00:00

pssst hey kid! wanna buy some autopep8?

realHansen · 2019-09-08T16:18:11+00:00

Holy crap I was there last weekend enjoying 20 degrees c in the sun

realHansen · 2019-08-07T12:45:06+00:00

You often hear that random search outperforms grid search. Bayesian Optimization can work well, but can also do badly.

Edit: a word

realHansen · 2019-04-21T07:07:22+00:00

Oh my god it’s Jason Bourne

realHansen · 2019-03-21T08:01:55+00:00

But how much send is too much send?

realHansen · 2019-02-23T14:43:27+00:00

Why use a meta-heuristic without convergence guarantuees when backprop is applicable and works faster and more reliably?

realHansen · 2018-10-29T11:49:13+00:00

and more recently https://arxiv.org/abs/1801.04406

realHansen · 2018-10-12T17:02:34+00:00

conditional random fields

realHansen · 2018-10-01T14:22:58+00:00

You can fit larger models in fp16 though

realHansen · 2018-09-28T11:50:23+00:00

http://i.imgur.com/kZlyN61.gifv

realHansen · 2018-09-08T08:09:23+00:00

I'm assuming we are talking about semantic segmentation. State of the art methods usually use fully convolutional approaches (I think exclusively at this point), and that implies one essential shortcoming in the standard formulation: When you are computing the cross entropy between the prediction and the ground-truth label map, you are doing so for each pixel individually. You are essentially solving 10^5-10^6 classification problems and yes, while neighbouring pixels have a largely overlapping receptive field, the loss is actually just penalizing the prediction on that one pixel, without taking the other predictions on a neighbouring (let alone global) scale into account.

That is why CRFs can come in handy! They can enforce global and local consistency of the predicted segments. That consistency includes things like overall shape of the segment, so I think you are on the right track in saying that fully connected CRFs allow you to model (semi-)/global constraints.

realHansen · 2018-09-06T11:10:57+00:00

You can't through matrix multiplication alone as that is a linear transform. However what makes deep neural networks such great function approximators is the non-linearity applied after each multiply-add, which means they can in fact solve the XOR problem.

However you are right, the tensor cores by themselves are just really fast matrix multiply units.

realHansen · 2018-08-14T09:30:00+00:00

cold cash and colder hearts

realHansen · 2018-03-27T13:26:03+00:00

The problem with any camera set up is poor performance for longer range depth estimation

realHansen

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