9 citations · 16 across the 7 of their papers we have counts for
7 papers
FedFrozen: Two-Stage Federated Optimization via Attention Kernel Freezing
Junye Du, Zhenghao Li, Yushi Feng +1
Federated learning with heterogeneous clients remains a significant challenge for deep learning, primarily due to client drift arising from inconsistent local updates. Existing fed…
HEDP: A Hybrid Energy-Distance Prompt-based Framework for Domain Incremental Learning
Yu Feng, Zhen Tian, Haoran Luo +8
Domain Incremental Learning is a critical scenario that requires models to continuously adapt to new data domains without retraining. However, domain shifts often cause severe perf…
The activity-weight duality in feed forward neural networks: The geometric determinants of generalization
Yu Feng, Yuhai Tu
One of the fundamental problems in machine learning is generalization. In neural network models with a large number of weights (parameters), many solutions can be found to fit the…
Loss Landscape Dependent Self-Adjusting Learning Rates in Decentralized Stochastic Gradient Descent
Wei Zhang, Mingrui Liu, Yu Feng +3
Distributed Deep Learning (DDL) is essential for large-scale Deep Learning (DL) training. Synchronous Stochastic Gradient Descent (SSGD) 1 is the de facto DDL optimization method.…
Storage capacity of networks with discrete synapses and sparsely encoded memories
Yu Feng, Nicolas Brunel
Attractor neural networks (ANNs) are one of the leading theoretical frameworks for the formation and retrieval of memories in networks of biological neurons. In this framework, a p…
Phases of learning dynamics in artificial neural networks: with or without mislabeled data
Yu Feng, Yuhai Tu
Despite tremendous success of deep neural network in machine learning, the underlying reason for its superior learning capability remains unclear. Here, we present a framework base…
How neural networks find generalizable solutions: Self-tuned annealing in deep learning
Yu Feng, Yuhai Tu
Despite the tremendous success of Stochastic Gradient Descent (SGD) algorithm in deep learning, little is known about how SGD finds generalizable solutions in the high-dimensional…