DenseAMs show tradeoffs between entropy production, retrieval accuracy, and speed at intermediate loads, with a new failure mode in higher-order networks at finite temperature.
Amit, Hanoch Gutfreund, and Haim Sompolinsky
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Random Matrix Theory detects overfitting via growing Correlation Traps in weight spectra during the anti-grokking phase of neural network training.
TETRIS-ADAPT-VQE achieves fidelities above 99.3% for SYK (N=20) and 99.9998% for SK (L=18) but requires large resources for SYK models.
Thesis uses statistical mechanics to study DAM and RBM models for understanding memorization, low-dimensional learning, and adversarial robustness in neural networks.
citing papers explorer
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Stochastic Thermodynamics of Associative Memory
DenseAMs show tradeoffs between entropy production, retrieval accuracy, and speed at intermediate loads, with a new failure mode in higher-order networks at finite temperature.
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Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory
Random Matrix Theory detects overfitting via growing Correlation Traps in weight spectra during the anti-grokking phase of neural network training.
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Ground state preparation of random all-to-all Hamiltonians using ADAPT-VQE
TETRIS-ADAPT-VQE achieves fidelities above 99.3% for SYK (N=20) and 99.9998% for SK (L=18) but requires large resources for SYK models.
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Explaining Machine Learning and Memorization with Statistical Mechanics
Thesis uses statistical mechanics to study DAM and RBM models for understanding memorization, low-dimensional learning, and adversarial robustness in neural networks.