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Found 5 papers, 1 papers with code
DSS: Synthesizing long Digital Ink using Data augmentation, Style encoding and Split generation
As text generative models can give increasingly long answers, we tackle the problem of synthesizing long text in digital ink.
Sampling and Ranking for Digital Ink Generation on a tight computational budget
We use and compare the effect of multiple sampling and ranking techniques, in the first ablation study of its kind in the digital ink domain.
Can we learn gradients by Hamiltonian Neural Networks?
In this work, we propose a meta-learner based on ODE neural networks that learns gradients.
Towards Model Agnostic Federated Learning Using Knowledge Distillation
Is it possible to design an universal API for federated learning using which an ad-hoc group of data-holders (agents) collaborate with each other and perform federated learning?
Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation and Optimal Measurement Placement in Power Systems?
We consider a power transmission system monitored with Phasor Measurement Units (PMUs) placed at significant, but not all, nodes of the system.
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