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Found 10 papers, 6 papers with code
Learning Non-myopic Power Allocation in Constrained Scenarios
The problem of optimal power allocation -- for maximizing a given network utility metric -- under instantaneous constraints has recently gained significant popularity.
Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networks
We develop an efficient and near-optimal solution for beamforming in multi-user multiple-input-multiple-output single-hop wireless ad-hoc interference networks.
Stability Analysis of Unfolded WMMSE for Power Allocation
Power allocation is one of the fundamental problems in wireless networks and a wide variety of algorithms address this problem from different perspectives.
Label Propagation across Graphs: Node Classification using Graph Neural Tangent Kernels
In this context, our current work considers a challenging inductive setting where a set of labeled graphs are available for training while the unlabeled target graph is completely separate, i. e., there are no connections between labeled and unlabeled nodes.
GIST: Distributed Training for Large-Scale Graph Convolutional Networks
The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale both in terms of graph size and the number of model parameters.
Efficient power allocation using graph neural networks and deep algorithm unfolding
We study the problem of optimal power allocation in a single-hop ad hoc wireless network.
Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation
We study the problem of optimal power allocation in a single-hop ad hoc wireless network.
ChartNet: Visual Reasoning over Statistical Charts using MAC-Networks
To this end, we formulate the problem of reasoning over statistical charts as a classification task using MAC-Networks to give answers from a predefined vocabulary of generic answers.
Deep Reader: Information extraction from Document images via relation extraction and Natural Language
In this paper, we propose a novel enterprise based end-to-end framework called DeepReader which facilitates information extraction from document images via identification of visual entities and populating a meta relational model across different entities in the document image.
Optical Character Recognition
Optical Character Recognition (OCR)
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An Efficient End-to-End Neural Model for Handwritten Text Recognition
Offline handwritten text recognition from images is an important problem for enterprises attempting to digitize large volumes of handmarked scanned documents/reports.
