11 citations · 20 across the 6 of their papers we have counts for
7 papers
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems
Mao Ye, Ruichen Jiang, Haoxiang Wang +6
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…
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems
Xiaocong Du, Bhargav Bhushanam, Jiecao Yu +7
Deep learning recommendation systems at scale have provided remarkable gains through increasing model capacity (i.e. wider and deeper neural networks), but it comes at significant…
Structural Pruning in Deep Neural Networks: A Small-World Approach
Gokul Krishnan, Xiaocong Du, Yu Cao
Deep Neural Networks (DNNs) are usually over-parameterized, causing excessive memory and interconnection cost on the hardware platform. Existing pruning approaches remove secondary…
CGaP: Continuous Growth and Pruning for Efficient Deep Learning
Xiaocong Du, Zheng Li, Yu Cao
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 capa…
Towards Efficient Neural Networks On-a-chip: Joint Hardware-Algorithm Approaches
Xiaocong Du, Gokul Krishnan, Abinash Mohanty +3
Machine learning algorithms have made significant advances in many applications. However, their hardware implementation on the state-of-the-art platforms still faces several challe…
Single-Net Continual Learning with Progressive Segmented Training (PST)
Xiaocong Du, Gouranga Charan, Frank Liu +1
There is an increasing need of continual learning in dynamic systems, such as the self-driving vehicle, the surveillance drone, and the robotic system. Such a system requires learn…