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Found 17 papers, 4 papers with code
ADR-GNN: Advection-Diffusion-Reaction Graph Neural Networks
Graph neural networks (GNNs) have shown remarkable success in learning representations for graph-structured data.
DRIP: Deep Regularizers for Inverse Problems
First, most techniques cannot guarantee that the solution fits the data at inference.
Graph Positional Encoding via Random Feature Propagation
Two main families of node feature augmentation schemes have been explored for enhancing GNNs: random features and spectral positional encoding.
Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification
In this paper, we propose novel objective terms for the training of GNNs for node classification, aiming to exploit all the available data and improve accuracy.
Improving Graph Neural Networks with Learnable Propagation Operators
Graph Neural Networks (GNNs) are limited in their propagation operators.
Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks
Unsupervised image segmentation is an important task in many real-world scenarios where labelled data is of scarce availability.
Ranked #2 on
Unsupervised Semantic Segmentation
on COCO-Stuff-3
Estimating a potential without the agony of the partition function
In this paper we propose an alternative approach based on Maximum A-Posteriori (MAP) estimators, we name Maximum Recovery MAP (MR-MAP), to derive estimators that do not require the computation of the partition function, and reformulate the problem as an optimization problem.
pathGCN: Learning General Graph Spatial Operators from Paths
In the context of GCNs, differently from CNNs, a pre-determined spatial operator based on the graph Laplacian is often chosen, allowing only the point-wise operations to be learnt.
Rethinking Unsupervised Neural Superpixel Segmentation
Recently, the concept of unsupervised learning for superpixel segmentation via CNNs has been studied.
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are widely used in a variety of applications, and can be seen as an unstructured version of standard Convolutional Neural Networks (CNNs).
Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations
Quantization of Convolutional Neural Networks (CNNs) is a common approach to ease the computational burden involved in the deployment of CNNs, especially on low-resource edge devices.
PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations
Moreover, as we demonstrate using an extensive set of experiments, our PDE-motivated networks can generalize and be effective for various types of problems from different fields.
Mimetic Neural Networks: A unified framework for Protein Design and Folding
Recent advancements in machine learning techniques for protein folding motivate better results in its inverse problem -- protein design.
MGIC: Multigrid-in-Channels Neural Network Architectures
We present a multigrid-in-channels (MGIC) approach that tackles the quadratic growth of the number of parameters with respect to the number of channels in standard convolutional neural networks (CNNs).
DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling
Graph Convolutional Networks (GCNs) have shown to be effective in handling unordered data like point clouds and meshes.
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks
In practice, the input data and the hidden features consist of a large number of channels, which in most CNNs are fully coupled by the convolution operators.
Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods
This method not only allows us to analytically and compactly represent the object, it also confers on us the ability to overcome calibration related noise that originates from inaccurate acquisition parameters.
