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Works290 from public data
- Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks930
This work takes the skeleton as the input at each time slot and introduces a novel regularization scheme to learn the co-occurrence features of skeleton joints, and proposes a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons.
- A Sufficient Condition for Convergences of Adam and RMSProp470
An alternative easy-to-check sufficient condition is introduced, which merely depends on the parameters of the base learning rate and combinations of historical second-order moments, to guarantee the global convergence of generic Adam/RMSProp for solving large-scale non-convex stochastic optimization.
- PyTorch distributed468
Evaluations show that, when configured appropriately, the PyTorch distributed data parallel module attains near-linear scalability using 256 GPUs.
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- EdgeFed: Optimized Federated Learning Based on Edge Computing197
Inspired by edge computing, this work proposed edge federated learning (EdgeFed), which separates the process of updating the local model that is supposed to be completed independently by mobile devices, to improve the learning efficiency and decrease the global communication frequency.
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- DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training186
This work proposes a novel personalized federated learning framework in a decentralized (peer-to-peer) communication protocol named Dis-PFL, which employs personalized sparse masks to customize sparse local models on the edge to save the communication and computation cost.
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- MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation147
It is shown that even when using a simple first-order method on the mixed-level formulation, MiLeNAS can achieve a lower validation error for NAS problems, and architectures obtained by the method achieve consistently higher accuracies than those obtained from bilevel optimization.
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- A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning139
This survey argues that forgetting is a double-edged sword and can be beneficial and desirable in certain cases, such as privacy-preserving scenarios, and presents a more nuanced understanding of this phenomenon and highlights its potential advantages.
- Deep Model Fusion: A Survey134
A comprehensive survey to summarize the recent progress of deep model fusion/merging and analyzes the challenges of deep model fusion to illuminate the possible research directions in the future.
- Stochastic Client Selection for Federated Learning With Volatile Clients133
This article investigates the client selection problem under a volatile context, in which the local training of heterogeneous clients is likely to fail due to various kinds of reasons and in different levels of frequency, and proposes E3CS, a stochastic client selection scheme as a solution.
- Learning from models beyond fine-tuning114
This Review provides a comprehensive overview of current methods based on foundation models from the perspective of LFM, and emphasizes that maximizing the use of parametric knowledge in data-scarce scenarios is critical to LFM.
- Make Landscape Flatter in Differentially Private Federated Learning109
A novel DPFL algorithm named DP-FedSAM is proposed, which leverages gradient perturbation to mitigate the negative impact of DP, and empirically confirms that the algorithm achieves state-of-the-art (SOTA) performance compared with existing SOTA baselines in DPFL.
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- Application of the adaptive reuse potential model in Hong Kong: A case study of Lui Seng Chun105
This paper explores the relationships between financial, environmental and social parameters associated with building adaptive reuse by way of a case study of a heritage building in Hong Kong known as Lui Seng Chun.
- On Transforming Reinforcement Learning With Transformers: The Development Trajectory103
This survey collects and dissects recent advances concerning the transformation of RL with transformers (transformer-based RL (TRL), and examines the main applications of TRL in robotic manipulation, text-based games (TBGs), navigation, and autonomous driving.
- Improving the Model Consistency of Decentralized Federated Learning103
Two DFL algorithms named DFedSAM and DFed SAM-MGS are proposed to improve the performance of DFL and can achieve competitive performance compared with CFL methods and outperform existing DFL methods.
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- On Efficient Training of Large-Scale Deep Learning Models102
This survey presents a comprehensive review of the general techniques for training acceleration and provides a detailed analysis and discussion of future works on the development of general acceleration techniques, which could inspire us to re-think and design novel efficient paradigms.
- FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy90
The proposed FedSpeed merges the vanilla stochastic gradient with a perturbation computed from an extra gradient ascent step in the neighborhood, thereby alleviating the issue of local over-fitting and achieves the state-of-the-art (SOTA) performance on the general FL experimental settings than several baselines.
- Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape88
This work proposes a novel and general algorithm that adopts a dynamic regularizer to guarantee the local optima towards the global objective, which is meanwhile revised by the global Sharpness Aware Minimization (SAM) optimizer to search for the consistent flat minima.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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