Temporal Graph Representation Learning with Adaptive Augmentation Contrastive

no code implementations • 7 Nov 2023 • Hongjiang Chen, Pengfei Jiao, Huijun Tang, Huaming Wu

Temporal graph representation learning aims to generate low-dimensional dynamic node embeddings to capture temporal information as well as structural and property information.

Contrastive Learning Graph Representation Learning

HQNAS: Auto CNN deployment framework for joint quantization and architecture search

no code implementations • 16 Oct 2022 • Hongjiang Chen, Yang Wang, Leibo Liu, Shaojun Wei, Shouyi Yin

Deep learning applications are being transferred from the cloud to edge with the rapid development of embedded computing systems.

Neural Architecture Search Quantization

FAQS: Communication-efficient Federate DNN Architecture and Quantization Co-Search for personalized Hardware-aware Preferences

no code implementations • 16 Oct 2022 • Hongjiang Chen, Yang Wang, Leibo Liu, Shaojun Wei, Shouyi Yin

Due to user privacy and regulatory restrictions, federate learning (FL) is proposed as a distributed learning framework for training deep neural networks (DNN) on decentralized data clients.

Neural Architecture Search Quantization