Love to see this course which puts data first. Looking forward to learning something new.

Copyright protection for fictional characters

>Copyright protection is available to the creators of a range of works including literary, musical, dramatic and artistic works. Recognition of fictional characters as works eligible for copyright protection has come about with the understanding that characters can be separated from the original works they were embodied in and acquire a new life by featuring in subsequent works.

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There's no information-theoretic notion of character copyright, for example. It's a game of calvinball, and has been since the Stationers' Company. It's true that copyright is badly misunderstood and over-generalized to things that it has absolutely nothing to do with, like plagiarism and other notions of (nonexistent) authorship rights, but it isn't a measurable thing either and you can't guarantee that the law and policy will agree with your model.

Might not be what you want, but you can use our hosted flan-t5 models at deepinfra.com. This way you can just call them as API. Even flan-t5-xxl. Disclaimer I work at Deep Infra.

Mlops is the datacentric course developed by andrew ng last year. Its at coursera fyi

So now there are at least two. Nice.

So it is like an ability to capture "correlations" that cannot be done with random forests.

I literally gave an example of how (C)NNs have better inductive bias than random forests for images.

You need to ask more precise questions than just a "why".

And those of us who taught ourselves need it even more. Love me some open source learning

Still, why is it that DL has better inductive biases than others?

Good list. Props for dust.tt weird but good

You are implying that the NN learns exp(logits) instead of the logits without really constraining the outputs to be positive. It probably won't be a proper scoring rule though might appear to work.

In some ways, this is similar to how you can learn classifiers with the mean squared error by regressing directly to the one-hot vector of class label (here also you don't care about positive output). It works, and also implies a proper scoring rule called the Brier score.

All model classes have inductive biases. e.g. random forests have the inductive bias of producing axis-aligned region splits. But clearly, that inductive bias is not good enough for image classification because a lot of information in the pixels is spatially correlated that axis-aligned regions cannot capture as specialized neural networks, under the same budget. By budget, I mean things like training time, model capacity, etc.

If we have infinite training time and infinite number of image samples, then probably random forests might be just as good as neural networks.

I agree. Note that overall there are two things we can hope for: 1. Using this approach with a appropriate k removes most of the "obvious" copyright violations and 2. for the remaining images the value k can be interpreted to determine whether there was a copyright violation or not, where the interpretation will necessarily be application and context dependent.

That's correct.

Yes, you can see animations of the general case on the github page.

This is really interesting work. I wonder how meaningful these metrics can be made. For example, if I write a book about Harry Potter, the expert mug maker, then your metric is x. If I write about Barry Blotter, the boy wizard at Smogwarts, then your metric is y. IANAL but I think that the value needed to prove derivative work is a question of fact that would be up to a jury to decide, so being able to explain such a metric to laypeople could make this work. It’s somewhat similar to the way that DNA testing required a certain amount of education for juries (one in a million match and all that)

There was a really good paper a few years ago that identifies some biases in how DNNs learn might explain why they work so well in practice as compared to alternatives. Essentially they are biased towards smoother solutions, which is often what is wanted.

This is still an area of active research, though. I think it's fair to say we still don't quite know why DNNs work as well as they do.

Amazing article. Thanks for sharing

It is. See Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

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It should be all continious functions, but I can't really think of any problems where this would limit the solution. The set of all continuous functions is a very big set!

As a side note, I think it's quite interesting that the theorem doesn't include periodic functions like sin, so I guess it's not quite all continuous functions, just continuous functions with bounded input.

It is not uncommon anymore to model images as patches of tokens, and then send in the sequence to a transformer-based model. So not psychotic at all.

See An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Hi all, I am looking for datasets on climate patterns in sub Saharan west Africa as well as data sets on crop production and yields for the same area. Any good sources available?

See Theorem 2 (Page 34) of The Supervised Learning No-Free-Lunch Theorems.

It conditions "uniformly" averaged over all "f" the input-output mapping, i.e. the function that generates the dataset (this is a noise-free case). It also provides "uniformly averaged over all P(f)", a distribution over the data-generating functions.

So while you could still have different data-generating distributions P(f), the result is defined over all such distributions uniformly averaged.

The NFL is sort of a worst-case result, and I think it pretty meaningless and inconsequential for the real world.

Let me know if I have misinterpreted this!

In my experience DNNs only help with structured data (audio, video, images etc.). I once had a large (~10M datapoints) tabular dataset and found that simply taking a random 2K subset and fitting an SVM gave the best results. I think this is usually the case, but people still want DNNs for some reason. If it were a vision problem, then, of course, it'd be the other way around.

It's worth noting that it wasn't until conv nets that DNNs took off. It's hard to think of a problem that a traditional vanilla MLP solves that can't also be solved with an SVM.