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Found 6 papers, 1 papers with code
MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale
Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy function of interest.
SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points.
Efficient Halftoning via Deep Reinforcement Learning
In this paper, we propose a fast and structure-aware halftoning method via a data-driven approach.
FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training
A key performance bottleneck when training graph neural network (GNN) models on large, real-world graphs is loading node features onto a GPU.
WaveIPT: Joint Attention and Flow Alignment in the Wavelet domain for Pose Transfer
To leverage the advantages of both attention and flow simultaneously, we propose Wavelet-aware Image-based Pose Transfer (WaveIPT) to fuse the attention and flow in the wavelet domain.
Halftoning with Multi-Agent Deep Reinforcement Learning
Deep neural networks have recently succeeded in digital halftoning using vanilla convolutional layers with high parallelism.
