A masked graph autoencoder on heterogeneous bidirectional graphs predicts per-flow NetFlow attachments and features from sliding windows of network traffic.
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
CNN-attention model decodes EEG to hand kinematics with within-subject PCCs above 0.98 on two axes, improved to 0.93 overall by a motion-state FSM copilot that drops under 20% of points, enabling simulated Franka Panda control.
citing papers explorer
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Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder
A masked graph autoencoder on heterogeneous bidirectional graphs predicts per-flow NetFlow attachments and features from sliding windows of network traffic.
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Copilot-Assisted Second-Thought Framework for Brain-to-Robot Hand Motion Decoding
CNN-attention model decodes EEG to hand kinematics with within-subject PCCs above 0.98 on two axes, improved to 0.93 overall by a motion-state FSM copilot that drops under 20% of points, enabling simulated Franka Panda control.