<title>neural-scaling on Kyle Roth</title>

<description>Recent content in neural-scaling on Kyle Roth</description>

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<guid>https://kylrth.com/paper/palm/</guid>

<description>This was a paper I presented about in Bang Liu’s research group meeting on 2022-04-11. You can view the slides I used here.</description>

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<title>Scaling laws for the few-shot adaptation of pre-trained image classifiers</title>

<guid>https://kylrth.com/paper/scaling-laws-few-shot-image-classifiers/</guid>

<description>The unsurprising result here is that few-shot performance scales predictably with pre-training dataset size under traditional fine-tuning, matching network, and prototypical network approaches. The interesting result is that the exponents of these three approaches were substantially different (see Table 1 in the paper), which says to me that the few-shot inference approach matters a lot. The surprising result was that while more training on the “non-natural” Omniglot dataset did not improve few-shot accuracy on other datasets, training on “natural” datasets did improve accuracy on few-shot Omniglot.</description>

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<title>In search of robust measures of generalization</title>

<guid>https://kylrth.com/paper/robust-measures-of-generalization/</guid>

<description>These authors define robust error as the least upper bound on the expected loss over a family of environmental settings (including dataset, model architecture, learning algorithm, etc.): \[\sup_{e\in\mathcal F}\mathbb E_{\omega\in P^e}\left[\ell(\phi,\omega)\right]\] The fact that this is an upper bound and not an average is very important and is what makes this work unique from previous work in this direction. Indeed, what we should be concerned about is not how poorly a model performs on the average sample but on the worst-case sample.</description>

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<title>It's not just size that matters: small language models are also few-shot learners</title>

<guid>https://kylrth.com/paper/not-just-size-that-matters/</guid>

<description>We presented this paper as a mini-lecture in Bang Liu’s IFT6289 course in winter 2022. You can view the slides we used here.</description>

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<title>Scaling laws for transfer</title>

<guid>https://kylrth.com/paper/scaling-laws-for-transfer/</guid>

<description>This post was created as an assignment in Irina Rish’s neural scaling laws course (IFT6167) in winter 2022. The post contains no summarization, only questions and thoughts. Sometimes these scaling laws can feel like pseudoscience because they’re a post hoc attempt to place a trend line on data. How can we be confident that the trends we observe actually reflect the scaling laws that we’re after? In the limitations section they mention that they didn’t tune hyperparameters for fine-tuning or for the code data distribution.</description>

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<title>Deep learning scaling is predictable, empirically</title>

<guid>https://kylrth.com/paper/scaling-predictable-empirically/</guid>

<description>This was a paper we presented about in Irina Rish’s neural scaling laws course (IFT6167) in winter 2022. You can view the slides we used here. It’s important to note that in the results for NMT (Figure 1) we would expect the lines in the graph on the left to curve as the capacity of the individual models is exhausted. That’s why the authors fit the curves with an extra constant added.</description>

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<title>Data scaling laws in NMT: the effect of noise and architecture</title>

<guid>https://kylrth.com/paper/data-scaling-laws-nmt/</guid>

<description>This paper is all about trying a bunch of different changes to the training setup to see what affects the power law exponent over dataset size. Here are some of the answers: encoder-decoder size asymmetry: exponent not affected, but effective model capacity affected architecture (LSTM vs. Transformer): exponent not affected, but effective model capacity affected dataset quality (filtered vs. not): exponent and effective model capacity not effected, losses on smaller datasets affected dataset source (ParaCrawl vs.</description>

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<title>Parallel training of deep networks with local updates</title>

<guid>https://kylrth.com/paper/parallel-training-with-local-updates/</guid>

<description>This post was created as an assignment in Irina Rish’s neural scaling laws course (IFT6167) in winter 2022. The post contains no summarization, only questions and thoughts. Once I learned how the loss functions worked for each chunk, my first question was whether the earlier chunks were going to be able to learn the low-level features that later chunks would need. Figure 7 seems to show that they do, although their quality apparently decreases with increasingly local updates.</description>

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<title>Learning transferable visual models from natural language supervision (CLIP)</title>

<guid>https://kylrth.com/paper/clip/</guid>

<description>This post was created as an assignment in Irina Rish’s neural scaling laws course (IFT6167) in winter 2022. The post contains no summarization, only questions and thoughts. This concept of wide vs. narrow supervision (rather than binary “supervised” and “unsupervised”) is an interesting and flexible way to think about the way these training schemes leverage data. The zero-shot CLIP matches the performance of 4-shot CLIP, which is a surprising result. What do the authors mean when they make this guess about zero-shot’s advantage:</description>

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