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Found 9 papers, 6 papers with code
MGNNI: Multiscale Graph Neural Networks with Implicit Layers
Recently, implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs.
Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis
In this paper, we propose the CORE (Counterfactual Analysis based Relation Extraction) debiasing method that guides the RE models to focus on the main effects of textual context without losing the entity information.
Dangling-Aware Entity Alignment with Mixed High-Order Proximities
We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem.
EIGNN: Efficient Infinite-Depth Graph Neural Networks
Motivated by this limitation, we propose a GNN model with infinite depth, which we call Efficient Infinite-Depth Graph Neural Networks (EIGNN), to efficiently capture very long-range dependencies.
OneFlow: Redesign the Distributed Deep Learning Framework from Scratch
Aiming at a simple, neat redesign of distributed deep learning frameworks for various parallelism paradigms, we present OneFlow, a novel distributed training framework based on an SBP (split, broadcast and partial-value) abstraction and the actor model.
A Fusion-Denoising Attack on InstaHide with Data Augmentation
InstaHide is a state-of-the-art mechanism for protecting private training images, by mixing multiple private images and modifying them such that their visual features are indistinguishable to the naked eye.
LSCALE: Latent Space Clustering-Based Active Learning for Node Classification
We argue that the representation power in unlabelled nodes can be useful for active learning and for further improving performance of active learning for node classification.
RocNet: Recursive Octree Network for Efficient 3D Deep Representation
Our network compresses a voxel grid of any size down to a very small latent space in an autoencoder-like network.
Incremental Kernel Null Space Discriminant Analysis for Novelty Detection
This validates the superiority of our IKNDA against the state of the art in novelty detection for large-scale data.
