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Found 7 papers, 3 papers with code
Deep Pattern Network for Click-Through Rate Prediction
These patterns harbor substantial potential to significantly enhance CTR prediction performance.
Modem Optimization of High-Mobility Scenarios: A Deep-Learning-Inspired Approach
The next generation wireless communication networks are required to support high-mobility scenarios, such as reliable data transmission for high-speed railways.
Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction, crucial in applications like recommender systems and online advertising, involves ranking items based on the likelihood of user clicks.
Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation
In the fusion step, advanced neural networks are used to model the hierarchical correlations between user behaviors.
Data Augmentation of Bridging the Delay Gap for DL-based Massive MIMO CSI Feedback
In massive multiple-input multiple-output (MIMO) systems under the frequency division duplexing (FDD) mode, the user equipment (UE) needs to feed channel state information (CSI) back to the base station (BS).
Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks
Sequential recommendation (SR) plays an important role in personalized recommender systems because it captures dynamic and diverse preferences from users' real-time increasing behaviors.
Attention Spiking Neural Networks
On ImageNet-1K, we achieve top-1 accuracy of 75. 92% and 77. 08% on single/4-step Res-SNN-104, which are state-of-the-art results in SNNs.
