Search Results for author:
Found 8 papers, 5 papers with code
Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models
To address this challenge, we propose a novel method ZSCL to prevent zero-shot transfer degradation in the continual learning of vision-language models in both feature and parameter space.
PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud
In this work, we establish PIDS, a novel paradigm to jointly explore point interactions and point dimensions to serve semantic segmentation on point cloud data.
FADE: Enabling Large-Scale Federated Adversarial Training on Resource-Constrained Edge Devices
Adversarial Training (AT) has been proven to be an effective method of introducing strong adversarial robustness into deep neural networks.
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor
MAKPConv employs a depthwise kernel to reduce resource consumption and re-calibrates the contribution of kernel points towards each neighbor point via Neighbor-Kernel attention to improve representation power.
Machine Learning for Electronic Design Automation: A Survey
With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing.
AEGCN: An Autoencoder-Constrained Graph Convolutional Network
In extensive experiments on citation networks and other heterogeneous graphs, we demonstrate that adding autoencoder constraints significantly improves the performance of graph convolutional networks.
The Graph-Based Behavior-Aware Recommendation for Interactive News
First, we build an interaction behavior graph for multi-level and multi-category data.
Scalable Peaceman-Rachford Splitting Method with Proximal Terms
Along with developing of Peaceman-Rachford Splittling Method (PRSM), many batch algorithms based on it have been studied very deeply.
