EventNMF models event data as Poisson processes with intensities factorized via non-negative B-spline bases to recover shared temporal templates directly from event times.
Cunningham and Byron M
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
LLMs exhibit Bayesian-like hypothesis updating with strong-sampling bias and an evaluation-generation gap but generalize poorly outside observed data.
A neural-network approximation of heteroclinic dynamics, interpretable as an Amari-type neural-field system, reproduces sequential transitions among cognitive states.
citing papers explorer
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Non-Negative Matrix Factorization for Event Data
EventNMF models event data as Poisson processes with intensities factorized via non-negative B-spline bases to recover shared temporal templates directly from event times.
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Hypothesis generation and updating in large language models
LLMs exhibit Bayesian-like hypothesis updating with strong-sampling bias and an evaluation-generation gap but generalize poorly outside observed data.
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Modeling sequential cognitive states via population level cortical dynamics
A neural-network approximation of heteroclinic dynamics, interpretable as an Amari-type neural-field system, reproduces sequential transitions among cognitive states.