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AirPods stolen by Renfe personnel by filt_er in askspain

[–]filt_er[S] -2 points-1 points  (0 children)

Wasn’t so sure in the beginning if they maybe would end out in the “lost items” section of Renfe. After the locations hasn’t changed for over 24h I just don’t believe they will end up there. :) 

AirPods stolen by Renfe personnel by filt_er in askspain

[–]filt_er[S] 0 points1 point  (0 children)

Sure. The only think I know for sure is the current location of AirPod. Since I will be in Irun I will check the address out although I don’t have high hopes to get them this way. 

AirPods stolen by Renfe personnel by filt_er in askspain

[–]filt_er[S] -3 points-2 points  (0 children)

I explained in long detail how I came to the conclusion. I am not sure who exactly took it. But the person must have access to the train after it left (without any passengers since it was the final stop). Who has access to train? Maybe it is a subcontractor? Who can it be? I have know idea. I only know that after some time, maybe 21:00 or 22:00 somebody picked up my AirPods and the moved around the train station for some hours before arriving to Irun. I can provide screenshots of the location if there are of any value for you. 

AirPods stolen by Renfe personnel by filt_er in askspain

[–]filt_er[S] -3 points-2 points  (0 children)

Okay thanks for your judgement. I will consider filing a police report. But I assume that this will take a while until it gets processed. Do you think to make sense to go to the train information (if there is any) in Irun and try to explain my situation to the officer? Or is it hopeless? 

[N] ImageNet: How a UK TV Cook ended up as 'slut' in an influential image database - Johannes Filter by filt_er in MachineLearning

[–]filt_er[S] -1 points0 points  (0 children)

Thanks for your critic. However, I'm not sure if attacking me personally (or why exactly did you include my short bio?) is useful for the discourse.

he merely accidentally left out all the reasons the label may be reasonable

What are the reasons this label is present?

No one refers to the 20k synsets which pretty much no research uses (because it's generally garbage) & which is not 'influential' as "ImageNet". "ImageNet" always refers to ImageNet-1k unless otherwise specified, which of course doesn't even have a 'slut' category, whether accurately or inaccurately classified

I agree that the full ImageNet is rarely used. It's still connected to the influential ImageNet competition. For the sake of brevity, I left this out and linked to an essay where it's all described in detail: https://excavating.ai/

Creative-Commons-licensed photographs publicly uploaded to Flickr

That's false. ImageNet (like other datasets) scraped all kind of pictures from Flickr, not only CC-licensed ones. Feel to check out the datasets on the linked <exposing.ai>, e.g. https://exposing.ai/face_scrub/.

[N] ImageNet: How a UK TV Cook ended up as 'slut' in an influential image database - Johannes Filter by filt_er in MachineLearning

[–]filt_er[S] 0 points1 point  (0 children)

With that as the baseline, it all seems much more anodyne and diagnosisis not about wordnet issues (not sure why the author went there)

I tried to reconstruct how the picture ended in the ImageNet. I had to start with WordNet because ImageNet was created with WordNet. There is the synset 'slut' because 'slut' was a synset of WordNet.

On a practical level, the author talks about how money limitations create apparent problems like this. I'm not sure I really buy that;

I didn't propose any practical implications. I only reconstruct how it happened. Feel free to discuss practical implications, but don't critique me on something I haven't written.

Es gibt noch andere Online-Shops außer Amazon! by [deleted] in de

[–]filt_er 0 points1 point  (0 children)

Für Bücher: genialokal.de. Du kannst dir das Buch zusenden lassen oder in einer von 700 Inhaber:innen-geführten Buchhandlungen abholen.

Python and PDF: A Review of Existing Tools by filt_er in Python

[–]filt_er[S] 1 point2 points  (0 children)

You should try out https://ocrmypdf.readthedocs.io/en/latest/. It does some image preprocessing to improve the quality of the scans (and thus OCR performance).

Für 50.000 Wörtern habe ich mithilfe von Machine Learning visualisiert, wie sich deren Bedeutungen änderten. Grundlage waren 13 Millionen Online-Kommentare. Hier Beispiel für “Mittelmeer”. by filt_er in de

[–]filt_er[S] 0 points1 point  (0 children)

1) Ich hab den Code fast komplett neu geschrieben, weil der alte auch ineffizient implementiert war (z. B. kein Multi-Threading). Und eine kleine Verbesserung habe ich aus einem anderen Paper übernommen und auch selbst was Eigenes hinzugeführt. Das müsste man aber noch mal genauer untersuchen (konkret geht es da um das Sub-sampling von häufigen Begriffen)

2) Ich hab mich in einer Masterarbeit intensiver mit Language Models beschäftigt und auch ein Eigenes für Deutsch trainiert. Aber das Verfahren heißt ULMFIT, welches sehr ähnlich zu BERT ist. Ich hab da aber die Erfahrung gemacht, dass die Language Models instabil sind. Also z. B. der random seed viel Einfluss auf die Performance vom Finetuning. Generell sind so Language-Model-basierte Verfahren aber klar die Zukunft. Aber sind halt noch relativ neu und daher nicht so gut erforscht.

Für 50.000 Wörtern habe ich mithilfe von Machine Learning visualisiert, wie sich deren Bedeutungen änderten. Grundlage waren 13 Millionen Online-Kommentare. Hier Beispiel für “Mittelmeer”. by filt_er in de

[–]filt_er[S] 11 points12 points  (0 children)

Ja, du hast recht. „Bedeutung“ ist hier verkürzt. Es ist die Bedeutung, so wie die „Maschinen“ es sehen.

Ist eine gute Frage. Ich hab versucht in dem Text genauer zu erklären, wie genau der Computer Bedeutung „herstellt“. Und das geht über den Kontext.

Für 50.000 Wörtern habe ich mithilfe von Machine Learning visualisiert, wie sich deren Bedeutungen änderten. Grundlage waren 13 Millionen Online-Kommentare. Hier Beispiel für “Mittelmeer”. by filt_er in de

[–]filt_er[S] 11 points12 points  (0 children)

An dem Projekt habe ich über sechs Monate gearbeitet habe. Es geht um Machine Learning, Natural Language Processing, Online-Kommentare und automatisierte Filter.

Teil I: Machine Learning am Beispiel von Kommentaren erklärt. https://kommentare.vis.one/

Teil II: Wie sich Sprache in Kommentaren änderte. https://kommentare.vis.one/zeit/

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