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Found 4 papers, 1 papers with code
Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Execution
In the quest for efficient neural network models for neural data interpretation and user intent classification in brain-computer interfaces (BCIs), learning meaningful sparse representations of the underlying neural subspaces is crucial.
SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems
Increasing the size of embedding layers has shown to be effective in improving the performance of recommendation models, yet gradually causing their sizes to exceed terabytes in industrial recommender systems, and hence the increase of computing and storage costs.
Target-centered Subject Transfer Framework for EEG Data Augmentation
In this paper, we propose a target-centered subject transfer framework as a data augmentation approach.
WiFi-based Multi-task Sensing
Then, for solving the imbalance issue, the extracted common feature in Wimuse is encouraged to get close to the counterpart features of the STS models.
