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Ablations are really important – Non_Interactive – Software & ML

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I don’t read as many papers as I once did. I find this surprising as I always assumed that when I made ML my full-time job, I would spend a lot more time reading up on all of the things that other folks in the field are up to. To some extent, this is a weakness. There is a healthy balance one should strike between reading and writing and I’m definitely skewing a bit too far towards the writing side of things (code, not papers). With that said, I have the honor of working with some of the people I respect most in the field, and they don’t read much more than I do. There are several good reasons for this, but the one I’d like to talk about today is the importance of slow progress and ablations in the field. These two things work harmoniously to make papers either too boring to read past a quick glance or completely unusable for any future work. Let’s talk about why. One of the most important things that I’ve learned over the last few years is the surprising capacity of neural networks to

I don’t read as many papers as I once did. I find this surprising as I always assumed that when I made ML my full-time job, I would spend a lot more time reading up on all of the things that other folks in the field are up to. To some extent, this is a weakness. There is a healthy balance one should strike between reading and writing and I’m definitely skewing a bit too far towards the writing side of things (code, not papers). With that said, I have the honor of working with some of the people I respect most in the field, and they don’t read much more than I do. There are several good reasons

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