Depth induces an implicit low-rank bias in deep unconstrained feature models trained with unregularized multiclass cross-entropy, promoting softmax codes over neural collapse via more efficient norm propagation.
and Johnson, Charles R
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Port-Hamiltonian neural networks extended to PDEs recover the Hamiltonian and dissipation of nonlinear string dynamics from data and outperform non-physics-informed baselines.
Faster quantum algorithm outputs a state whose energy is at most the minimum energy among all depth-d circuits applied to |0>, plus an energy estimate, for k-local Hamiltonians.
Analytical study derives conditions for ergodic infinite-temperature relaxation or robust discrete time crystal subharmonic oscillations in periodically kicked spin chains, depending on kicking protocol and initial state.
citing papers explorer
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The Implicit Bias of Depth: From Neural Collapse to Softmax Codes
Depth induces an implicit low-rank bias in deep unconstrained feature models trained with unregularized multiclass cross-entropy, promoting softmax codes over neural collapse via more efficient norm propagation.
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Identifying the nonlinear string dynamics with port-Hamiltonian neural networks
Port-Hamiltonian neural networks extended to PDEs recover the Hamiltonian and dissipation of nonlinear string dynamics from data and outperform non-physics-informed baselines.
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An Entropy-Governed Speedup for Quantum Algorithms on Local Hamiltonians
Faster quantum algorithm outputs a state whose energy is at most the minimum energy among all depth-d circuits applied to |0>, plus an energy estimate, for k-local Hamiltonians.
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Ergodic and Discrete Time Crystal Phases in Periodically Kicked Many-Body Quantum Systems: An Analytical Study
Analytical study derives conditions for ergodic infinite-temperature relaxation or robust discrete time crystal subharmonic oscillations in periodically kicked spin chains, depending on kicking protocol and initial state.