Showing Training dynamics Show all papers
Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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A representation-readout decomposition attributes grokking and double descent to competing encoder and classifier dynamics, showing delayed generalization stems from gradual representation learning rather than lazy-to-rich transitions.
Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Residual connections and symmetry-breaking nonlinearities cause geometric continuity across deep network layers, with activation and normalization distributing it differently across singular directions and projection types.
Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Architectural topology modifications eliminate Transformer's grokking phase by bounding representations and fixing attention, but only when aligned with task symmetries.
Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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Non-commuting SGD updates leave localized, steerable parametric memory of training order captured by gradient Lie brackets and readable via sparse vocabulary projections that identify model origins with 92% accuracy.
Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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A 2-datapoint reduced density matrix provides unified spectral early warnings of training phase transitions and interpretable eigenvectors across deep learning settings.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Gradient matching methods empirically estimate implicit regularization in deep networks, recovering explicit penalties and revealing dropout's implicit L2 effects.
Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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