252 citations · 259 across the 7 of their papers we have counts for
7 papers
Self-Supervised Quantization-Aware Knowledge Distillation
Kaiqi Zhao, Ming Zhao
Quantization-aware training (QAT) and Knowledge Distillation (KD) are combined to achieve competitive performance in creating low-bit deep learning models. However, existing works…
SGA: A Graph Augmentation Method for Signed Graph Neural Networks
Zeyu Zhang, Shuyan Wan, Sijie Wang +5
Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-world signed graphs containing positive and negative links. However, three key challenges hind…
Poster: Self-Supervised Quantization-Aware Knowledge Distillation
Kaiqi Zhao, Ming Zhao
Quantization-aware training (QAT) starts with a pre-trained full-precision model and performs quantization during retraining. However, existing QAT works require supervision from t…
Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions
Kaiqi Zhao, Animesh Jain, Ming Zhao
Pruning is a promising approach to compress deep learning models in order to deploy them on resource-constrained edge devices. However, many existing pruning solutions are based on…
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer Learning
Kaiqi Zhao, Yitao Chen, Ming Zhao
Knowledge Transfer (KT) achieves competitive performance and is widely used for image classification tasks in model compression and transfer learning. Existing KT works transfer th…
GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation
Song Yang, Jiamou Liu, Kaiqi Zhao
Next POI recommendation intends to forecast users' immediate future movements given their current status and historical information, yielding great values for both users and servic…