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Found 16 papers, 7 papers with code
ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks
Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method.
OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition
Depression Recognition (DR) poses a considerable challenge, especially in the context of the growing concerns surrounding privacy.
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
Building Category Graphs Representation with Spatial and Temporal Attention for Visual Navigation
To this end, an agent needs to 1) learn a piece of certain knowledge about the relations of object categories in the world during training and 2) look for the target object based on the pre-learned object category relations and its moving trajectory in the current unseen environment.
Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL
Based on extensive experiments, we find TSRL achieves great performance on small benchmark datasets with as few as 1% of the original samples, which significantly outperforms the recent offline RL algorithms in terms of data efficiency and generalizability. Code is available at: https://github. com/pcheng2/TSRL
AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing
In light of this, we propose an Alternating Graph-regularized Neural Network (AGNN) composed of Graph Convolutional Layer (GCL) and Graph Embedding Layer (GEL).
Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs
Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks. One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the quality of learned embeddings. Thus, in this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically studies meta-paths containing multi-hop neighbors from an adaptive aggregation of multi-order adjacency matrices.
Information Recovery-Driven Deep Incomplete Multiview Clustering Network
Concretely, a two-stage autoencoder network with the self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data.
DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification
To deal with the double incomplete multi-view multi-label classification problem, we propose a deep instance-level contrastive network, namely DICNet.
Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework
Graph convolutional networks (GCNs) have been attracting widespread attentions due to their encouraging performance and powerful generalizations.
Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-View Clustering
Graph-based multi-view clustering has attracted extensive attention because of the powerful clustering-structure representation ability and noise robustness.
Localized Sparse Incomplete Multi-view Clustering
Moreover, a novel local graph embedding term is introduced to learn the structured consensus representation.
Analyzing Wikipedia Membership Dataset and PredictingUnconnected Nodes in the Signed Networks
In the age of digital interaction, person-to-person relationships existing on social media may be different from the very same interactions that exist offline.
Lightweight image super-resolution with enhanced CNN
Finally, the IRB uses coarse high-frequency features from the RB to learn more accurate SR features and construct a SR image.
Ranked #45 on
Image Super-Resolution
on Set14 - 4x upscaling
Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation
The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks.
