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Found 8 papers, 0 papers with code
Harmless Transfer Learning for Item Embeddings
On the definition side, we reduce the bias in transfer loss by focusing on the items to which information from high-frequency items can be efficiently transferred.
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems
One of the key challenges of learning an online recommendation model is the temporal domain shift, which causes the mismatch between the training and testing data distribution and hence domain generalization error.
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems
Our method leverages structured sparsification to reduce computational cost without hurting the model capacity at the end of offline training so that a full-size model is available in the recurring training stage to learn new data in real-time.
Structural Pruning in Deep Neural Networks: A Small-World Approach
Inspired by the observation that brain networks follow the Small-World model, we propose a novel structural pruning scheme, which includes (1) hierarchically trimming the network into a Small-World model before training, (2) training the network for a given dataset, and (3) optimizing the network for accuracy.
Towards Efficient Neural Networks On-a-chip: Joint Hardware-Algorithm Approaches
Machine learning algorithms have made significant advances in many applications.
Single-Net Continual Learning with Progressive Segmented Training (PST)
Such a system requires learning from the data stream, training the model to preserve previous information and adapt to a new task, and generating a single-headed vector for future inference.
Efficient Network Construction through Structural Plasticity
A typical training pipeline to mitigate over-parameterization is to pre-define a DNN structure first with redundant learning units (filters and neurons) under the goal of high accuracy, then to prune redundant learning units after training with the purpose of efficient inference.
CGaP: Continuous Growth and Pruning for Efficient Deep Learning
Today a canonical approach to reduce the computation cost of Deep Neural Networks (DNNs) is to pre-define an over-parameterized model before training to guarantee the learning capacity, and then prune unimportant learning units (filters and neurons) during training to improve model compactness.
