stochastic gradient
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Knowledge Distillation Performs Partial Variance Reduction
Knowledge distillation is a popular approach for enhancing the performance of "student" models, with lower representational capacity, by taking advantage of
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On the Overlooked Structure of Stochastic Gradients Zeke Xie
Stochastic gradients closely relate to both optimization and generalization of deep neural networks (DNNs).
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Federated Multi-Objective Learning
In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications.
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Improved Schemes for Episodic Memory-based Lifelong Learning
On the other hand, a learning algorithm biasing towards old tasks will interfere with the learning of the new task. Several lines of methods are proposed recently to address this issue.
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