NeTMY neural fields with annealed encoding, multiscale optimization, and spectrum-fidelity losses achieve superior localization and distributional accuracy in NV-center inverse sensing by using a tensor power-summed dipolar operator that exposes and mitigates center-collapse failures.
Ridge regression: Biased estimation for nonorthogonal problems
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Extreme Quantum Cognition Machines combine quantum reservoir-style evolution with a dynamical attention mechanism in the Hamiltonian to produce robust nonlinear embeddings for decision making from noisy training data.
Negative-capable ridge regression uses controlled negative regularization as anti-shrinkage to increase effective complexity along weak eigendirections and mitigate underfitting in small-data regression.
An anytime algorithm for learning loss functions that is asymptotically optimal in the worst case and experimentally faster than prior methods for hyperparameter tuning.
Hierarchical model decomposes pure and global causal lifts in overlapping customer journeys via multiplicative factors and Monte Carlo estimation on 3M users, finding larger pure lifts and modest positive synergies.
citing papers explorer
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Neural Fields for NV-Center Inverse Sensing
NeTMY neural fields with annealed encoding, multiscale optimization, and spectrum-fidelity losses achieve superior localization and distributional accuracy in NV-center inverse sensing by using a tensor power-summed dipolar operator that exposes and mitigates center-collapse failures.
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Extreme Quantum Cognition Machines for Deliberative Decision Making
Extreme Quantum Cognition Machines combine quantum reservoir-style evolution with a dynamical attention mechanism in the Hamiltonian to produce robust nonlinear embeddings for decision making from noisy training data.
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A Ridge Too Far: Correcting Over-Shrinkage via Negative Regularization
Negative-capable ridge regression uses controlled negative regularization as anti-shrinkage to increase effective complexity along weak eigendirections and mitigate underfitting in small-data regression.
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Learning Effective Loss Functions Efficiently
An anytime algorithm for learning loss functions that is asymptotically optimal in the worst case and experimentally faster than prior methods for hyperparameter tuning.
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Hierarchical Causal Uplift Modeling in Overlapping Customer Journeys
Hierarchical model decomposes pure and global causal lifts in overlapping customer journeys via multiplicative factors and Monte Carlo estimation on 3M users, finding larger pure lifts and modest positive synergies.