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• W3174412103 abstract "We analyze the learning dynamics of infinitely wide neural networks with a finite sized bottle-neck. Unlike the neural tangent kernel limit, a bottleneck in an otherwise infinite width network al-lows data dependent feature learning in its bottle-neck representation. We empirically show that a single bottleneck in infinite networks dramatically accelerates training when compared to purely in-finite networks, with an improved overall performance. We discuss the acceleration phenomena by drawing similarities to infinitely wide deep linear models, where the acceleration effect of a bottleneck can be understood theoretically." @default.
• W3174412103 created "2021-07-05" @default.
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• W3174412103 date "2021-07-01" @default.
• W3174412103 modified "2023-09-27" @default.
• W3174412103 title "Implicit Acceleration and Feature Learning in Infinitely Wide Neural Networks with Bottlenecks." @default.
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