AI weather models may simulate the atmosphere via particle positions in latent space whose updates follow gradient flow on a learned free energy functional rather than conventional physical equations.
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6 Pith papers cite this work, alongside 8 external citations. Polarity classification is still indexing.
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Lipschitz continuous transformations F of probability measures w.r.t. Wasserstein distance admit continuous transport maps f(·,μ) such that F(μ) = f(·,μ)_# μ.
EMMETT and IRENE enable on-the-fly synthesis of classifiers for novel items in extreme classification, yielding up to 15% Recall@10 gains in zero-shot retrieval and 4.2% CTR lift in a production A/B test.
Introduces the Patnaik-Pearson intrinsic dimension estimator, proves some of its properties, relates it to HTSR/SETOL for Pareto spectra, and applies it to track embedding dimension evolution in BERT-base and DeepSeek-R1-Distill-Qwen-1.
Derives forward and backward propagation-of-chaos bounds for finite vs. infinite-context transformers modeled as contextual flow maps, achieving Wasserstein rate n^{-1/d} generally and n^{-1/2} for transformer-like cases.
Lightning self-attention coefficients are coordinates on an algebraic variety obeying Chow-type, low-rank, Veronese-type, and Sylvester-resultant invariants.
citing papers explorer
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The physics of AI weather models
AI weather models may simulate the atmosphere via particle positions in latent space whose updates follow gradient flow on a learned free energy functional rather than conventional physical equations.
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Continuous transformations of probability measures and their transport representations
Lipschitz continuous transformations F of probability measures w.r.t. Wasserstein distance admit continuous transport maps f(·,μ) such that F(μ) = f(·,μ)_# μ.
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Extreme Meta-Classification for Large-Scale Zero-Shot Retrieval
EMMETT and IRENE enable on-the-fly synthesis of classifiers for novel items in extreme classification, yielding up to 15% Recall@10 gains in zero-shot retrieval and 4.2% CTR lift in a production A/B test.
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Patnaik-Pearson intrinsic dimension for internal representations of neural networks
Introduces the Patnaik-Pearson intrinsic dimension estimator, proves some of its properties, relates it to HTSR/SETOL for Pareto spectra, and applies it to track embedding dimension evolution in BERT-base and DeepSeek-R1-Distill-Qwen-1.
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Propagation of Chaos in Contextual Flow Maps
Derives forward and backward propagation-of-chaos bounds for finite vs. infinite-context transformers modeled as contextual flow maps, achieving Wasserstein rate n^{-1/d} generally and n^{-1/2} for transformer-like cases.
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Algebraic Invariants of Lightning Self-Attention
Lightning self-attention coefficients are coordinates on an algebraic variety obeying Chow-type, low-rank, Veronese-type, and Sylvester-resultant invariants.