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.

169,051 papers · 148 categories

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5 results for “multi-network”

Deep Feature Learning of Multi-Network Topology for Node Classificationcs.LG

DeepMNE learns multi-network node features for better classification.

problem Learning node features across multiple networks.

method Semisupervised autoencoder for multi-network topology.

result DeepMNE outperforms state-of-the-art methods in node classification.

network embedding multi-network node classification autoencoder topology

2018-09-07abs ↗pdf ↗

Identifying the community structure of the international food-trade multi networkq-fin.GN

Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of food trade networks are still poorly documented, especially from a multi-network perspective. In particular,…

2017-11-15abs ↗pdf ↗

Deep learning of free boundary and Stefan problemsmath.NA

Proposes neural networks for solving complex free boundary problems.

problem Solving free boundary and Stefan problems with complex interfaces.

method Physics-informed neural networks for approximating solutions and boundaries.

result Successfully approximates solutions and moving boundaries in various Stefan problems.

physics-informed neural networks free boundary problems Stefan problems inverse problems deep learning

2020-06-04abs ↗pdf ↗

A deep learning framework for solution and discovery in solid mechanicscs.LG

Deep learning framework improves accuracy in solid mechanics.

problem Improving accuracy in solid mechanics simulations.

method Physics Informed Neural Networks (PINN) with multi-network model.

result PINN framework leads to more accurate predictions and improved robustness.

Physics Informed Neural Networks solid mechanics deep learning elasticity plasticity

2020-02-14abs ↗pdf ↗

Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolutioncs.LG

AdaGCN transfers labels across networks via adversarial domain adaptation and graph convolution.

problem Cross-network node classification with limited labeled data.

method Adversarial domain adaptation and graph convolution.

result AdaGCN successfully transfers labels with low labeled data on source networks and significant domain divergence.

graph transfer learning adversarial domain adaptation graph convolution cross-network node classification domain adaptation

2019-09-04abs ↗pdf ↗