Search Results for author:
Found 6 papers, 3 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.
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.
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.
Towards Open Temporal Graph Neural Networks
This will bring two big challenges to the existing dynamic GNN methods: (i) How to dynamically propagate appropriate information in an open temporal graph, where new class nodes are often linked to old class nodes.
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.
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.
