On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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2 results for “smoothness-promoting”

Kernel-based Graph Learning from Smooth Signals: A Functional Viewpointstat.ML

Proposes a novel graph learning framework for robust graph topology learning from graph signals.

problem Graph learning for revealing node relationships in data entities.

method Functional learning with smoothness-promoting graph learning, incorporating Kronecker product kernel.

result Improves robustness against missing and incomplete information in graph signals.

graph learning functional learning reproducing kernel Hilbert space Kronecker product kernel smoothness-promoting

2020-08-23abs ↗pdf ↗

Graph Structure Learning with Interpretable Bayesian Neural Networkscs.LG

Bayesian neural networks learn graph structure with interpretable parameters.

problem Learning graph structure from nodal observations in data with uncertainty.

method Introduces novel iterations with independently interpretable parameters and Bayesian neural networks.

result Bayesian neural networks provide well-calibrated uncertainty quantification on graph structure.

Bayesian neural networks graph structure learning interpretable parameters uncertainty quantification Markov Chain Monte Carlo

2024-06-20abs ↗pdf ↗