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Found 30 papers, 16 papers with code
On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving
However, the oversized neural networks render them impractical for deployment on resource-constrained systems, which unavoidably requires more computational time and resources during reference. To handle this, knowledge distillation offers a promising approach that compresses models by enabling a smaller student model to learn from a larger teacher model.
Rethinking Node-wise Propagation for Large-scale Graph Learning
However, (i) Most scalable GNNs tend to treat all nodes in graphs with the same propagation rules, neglecting their topological uniqueness; (ii) Existing node-wise propagation optimization strategies are insufficient on web-scale graphs with intricate topology, where a full portrayal of nodes' local properties is required.
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity
Then, each client conducts personalized training based on the local subgraph and the federated knowledge extractor.
FedGTA: Topology-aware Averaging for Federated Graph Learning
Existing FGL studies fall into two categories: (i) FGL Optimization, which improves multi-client training in existing machine learning models; (ii) FGL Model, which enhances performance with complex local models and multi-client interactions.
LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning
Most existing graph neural networks (GNNs) are limited to undirected graphs, whose restricted scope of the captured relational information hinders their expressive capabilities and deployments in real-world scenarios.
Towards Effective and General Graph Unlearning via Mutual Evolution
With the rapid advancement of AI applications, the growing needs for data privacy and model robustness have highlighted the importance of machine unlearning, especially in thriving graph-based scenarios.
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification
Despite numerous attempts, most existing GNNs struggle to achieve optimal node representations due to the constraints of undirected graphs.
Ranked #28 on
Node Classification
on Cornell
Learning to Generate Parameters of ConvNets for Unseen Image Data
Typical Convolutional Neural Networks (ConvNets) depend heavily on large amounts of image data and resort to an iterative optimization algorithm (e. g., SGD or Adam) to learn network parameters, which makes training very time- and resource-intensive.
Locally Differentially Private Graph Embedding
We propose LDP-GE, a novel privacy-preserving graph embedding framework, to protect the privacy of node data.
DREAM: Domain-free Reverse Engineering Attributes of Black-box Model
There is a crucial limitation: these works assume the dataset used for training the target model to be known beforehand and leverage this dataset for model attribute attack.
Shared Growth of Graph Neural Networks via Prompted Free-direction Knowledge Distillation
Knowledge distillation (KD) has shown to be effective to boost the performance of graph neural networks (GNNs), where the typical objective is to distill knowledge from a deeper teacher GNN into a shallower student GNN.
FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge
Upon training for a new task, the gradient integrator ensures the prevention of catastrophic forgetting and mitigation of negative knowledge transfer by effectively combining signature tasks identified from the past local tasks and other clients' current tasks through the global model.
Robust Knowledge Adaptation for Dynamic Graph Neural Networks
By this means, we can adaptively propagate knowledge to other nodes for learning robust node embedding representations.
Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration
To this end, we propose a Face Restoration Searching Network (FRSNet) to adaptively search the suitable feature extraction architecture within our specified search space, which can directly contribute to the restoration quality.
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks
Knowledge distillation (KD) has demonstrated its effectiveness to boost the performance of graph neural networks (GNNs), where its goal is to distill knowledge from a deeper teacher GNN into a shallower student GNN.
Blind Face Restoration: Benchmark Datasets and a Baseline Model
To address this problem, we first synthesize two blind face restoration benchmark datasets called EDFace-Celeb-1M (BFR128) and EDFace-Celeb-150K (BFR512).
Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering
Inheriting the advantages from information bottleneck, SIB-MSC can learn a latent space for each view to capture common information among the latent representations of different views by removing superfluous information from the view itself while retaining sufficient information for the latent representations of other views.
SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation
Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the supervised model trained on a labeled source domain.
Ranked #4 on
Semantic Segmentation
on GTAV-to-Cityscapes Labels
LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision
The prior art sheds light on exploring the accuracy-resource tradeoff by scaling the model sizes in accordance to resource dynamics.
Pareto Domain Adaptation
Domain adaptation (DA) attempts to transfer the knowledge from a labeled source domain to an unlabeled target domain that follows different distribution from the source.
Active Learning for Domain Adaptation: An Energy-Based Approach
Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains.
Deep Unsupervised Active Learning on Learnable Graphs
To make the learnt graph structure more stable and effective, we take into account $k$-nearest neighbor graph as a priori, and learn a relation propagation graph structure.
Learning Deep Representation with Energy-Based Self-Expressiveness for Subspace Clustering
In this paper, we propose a new deep subspace clustering framework, motivated by the energy-based models.
Causal Effect Estimation using Variational Information Bottleneck
A typical approach is to formulate causal inference as a supervised learning problem and so counterfactual could be predicted.
Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation
Specifically, we first design a pixel-wise contrastive loss by considering the correspondences between semantic distributions and pixel-wise representations from both domains.
Generalized Domain Conditioned Adaptation Network
Domain Adaptation (DA) attempts to transfer knowledge learned in the labeled source domain to the unlabeled but related target domain without requiring large amounts of target supervision.
On Deep Unsupervised Active Learning
Unsupervised active learning has attracted increasing attention in recent years, where its goal is to select representative samples in an unsupervised setting for human annotating.
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach
Spatial crowdsourcing (SC) utilizes the potential of a crowd to accomplish certain location based tasks.
Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution Networks
In addition, a pooling method based on percentile is proposed to improve the accuracy of the model.
Efficiently Indexing Large Sparse Graphs for Similarity Search
Abstract—The graph structure is a very important means to model schemaless data with complicated structures, such as protein- protein interaction networks, chemical compounds, knowledge query inferring systems, and road networks.
