Link degree distribution, symmetrized Hasse diagram Laplacian eigenvalues, and causal interval abundance distinguish nine classes of causal sets.
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QuBD extends algorithmic complexity estimation to quantized DNN weights, revealing that complexity decreases during learning, increases with overfitting, follows grokking patterns, and correlates with generalization.
Defines betweenness centrality in stochastic networks via absorbing Markov chain absorption times, estimated by Monte Carlo on random and real graphs.
A motif-based decomposition of quantile risk networks shows that local triadic topology and orbit-position diversity carry portfolio-relevant information missed by aggregate connectedness, with motif-based portfolios outperforming benchmarks and positional diversity marking tail transmitters.
Local 2- and 3-cycles enhance RNN computational capacity for Boolean functions, predicted by structural statistics, while adding interneurons boosts large networks.
Geo is a framework for optimizing graph pattern matching queries via rewrite rules and equality saturation that discovers equivalences and reduces costs by up to 99%.
citing papers explorer
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Charting causal set configuration space with graph observables
Link degree distribution, symmetrized Hasse diagram Laplacian eigenvalues, and causal interval abundance distinguish nine classes of causal sets.
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Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis
QuBD extends algorithmic complexity estimation to quantized DNN weights, revealing that complexity decreases during learning, increases with overfitting, follows grokking patterns, and correlates with generalization.
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Betweenness Central Nodes Under Uncertainty: An Absorbing Markov Chain Approach
Defines betweenness centrality in stochastic networks via absorbing Markov chain absorption times, estimated by Monte Carlo on random and real graphs.
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A Motif-Based Framework for Decomposing Risk Spillovers
A motif-based decomposition of quantile risk networks shows that local triadic topology and orbit-position diversity carry portfolio-relevant information missed by aggregate connectedness, with motif-based portfolios outperforming benchmarks and positional diversity marking tail transmitters.
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Identifying structural design principles shaping the computational abilities of recurrent neural networks
Local 2- and 3-cycles enhance RNN computational capacity for Boolean functions, predicted by structural statistics, while adding interneurons boosts large networks.
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Geo: A Query Rewrite Framework for Graph Pattern Mining
Geo is a framework for optimizing graph pattern matching queries via rewrite rules and equality saturation that discovers equivalences and reduces costs by up to 99%.