hashta

4 ms·

1.
by hashta 3mo ago
I used chatgpt's web app and I have pro subscription.
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by hashta 3mo ago
That's interesting because my experience has been almost the opposite. A few months ago I tested Gemini on converting screenshots of tables from PDF files into CSV. I tried it on several different tables and it got every one right. It
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by hashta 4mo ago
I think a lot of deep learning is compositional generalization. Models learn reusable pieces (abstractions, styles, procedures, constraints, etc) and recombine them in ways that may never have appeared as a whole in the training data. So ev
4.
by hashta 5mo ago
I think I trained models with #params >> #training examples for hundreds of epochs, but still don't recall seeing that loss curve on real data. Curious if others have seen it with larger models or much longer runs
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by hashta 5mo ago
Interesting read. I remember the grokking paper when it came out but I don't think I've ever seen that classic grokking loss curve in my own hands on real data. Curious if others have seen it more often in practice
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by hashta 9mo ago
this also addresses something else ... apple to some users "are you leaving for android because of their ai assistant? don’t leave we are bringing it to iphone"
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by hashta 9mo ago
I’m a long time Android user and almost switched to iPhone last year. Mostly because I use macOS and wanted better integration and also wanted to try it. Another big factor was the AI assistant. I stayed with Android because I think Google
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by hashta 1y ago
It’s literally called "SimpleFold". But that’s not really my point, from your earlier comment (".. go through all the complexities first to find the generalized and simpler formulations"), I got the impression you though
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by hashta 1y ago
To people outside the field, the title/abstract can make it sound like folding is just inherently simple now, but this model wouldn’t exist without the large synthetic dataset produced by the more complex AF. The "simple" arc
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by hashta 1y ago
I’m not sure AF3’s performance would hold up if it hadn’t been trained on data from AF2 which itself bakes in a lot of inductive bias like equivariance
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by hashta 1y ago
One caveat that’s easy to miss: the "simple" model here didn’t just learn folding from raw experimental structures. Most of its training data comes from AlphaFold-style predictions. Millions of protein structures that were themsel
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by hashta 2y ago
An effective way that usually increases accuracy is to use an ensemble of capable models that are trained independently (e.g., gemini, gpt-4o, qwen). If >x% of them have the same output, accept it, otherwise reject and manually review
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by hashta 3y ago
To both questions above, just simple averaging of the logits (classification) or raw outputs (regressions) usually works well. If I had to guess why people don't use this approach often in kaggle competitions is the relative difficulty
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by hashta 3y ago
I have a lot of experience working with both families of models. If you use an ensemble of 10 NNs, they outperform well-optimized tree-based models such as XGBoost & RFs.