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34.8
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Wang Weiyi
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kaupane
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Mtrya
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Embodied AI and whatever is fun
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One neural network. Five mathematical operations. Different learning times. We trained a small mixture-of-experts transformer on addition, subtraction, multiplication, division, and exponentiation modulo 113. The operations didn’t generalize together. Multiplication and division reached 90% held-out accuracy first, followed by exponentiation, subtraction, and finally addition. Multiplication before addition—the reverse of what we expect from a schoolchild. The accuracy chart in the linked article shows why a single overall accuracy number can hide an interesting learning story. Measured with IRIS from Saccadic LLC. More figures and analysis: https://huggingface.co/blog/Saccadic-AI/accuracy-plateaued-the-model-kept-changing Have you observed a similar ordering in multi-task grokking experiments?
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kaupane/relaion-art-recap-zh
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kaupane/artbench-captions
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kaupane/wikiart-captions
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