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Works17 from public data
- A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training34
This paper introduces a novel speed-up method for diffusion model training, called SpeeD, which is based on a closer look at time steps, and proposes a weighting strategy to emphasize the importance of time steps with rapid-change process increments.
- DD-Ranking: Rethinking the Evaluation of Dataset Distillation14
DD-Ranking, a unified evaluation framework, along with new general evaluation metrics to uncover the true performance improvements achieved by different methods are proposed, which provide a more comprehensive and fair evaluation standard for future research advancements.
- Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence14
Experiments on multiple datasets and models suggest that 3SFC has significantly better convergence rates compared to competing methods with lower compression rates and ablation studies and visualizations show that 3SFC can carry more information than competing methods for every communication round, further validating its effectiveness.
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- FLSGD: free local SGD with parallel synchronization10
This work proposes a novel method, free local stochastic gradient descent (FLSGD) with parallel synchronization, to eliminate the waiting and communication overhead of distributed DNN training and is the first work focusing on decreasing both distributed training load balancing and Communication overhead.
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- E-3SFC: Communication-Efficient Federated Learning With Double-Way Features Synthesizing5
A novel gradient compression method termed 3SFC is introduced, which utilizes the model itself as a decompressor, leveraging training priors such as model weights and objective functions, and which can significantly enhance communication efficiency in FL without compromising model performance.
- Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints5
Ferret is presented, a comprehensive framework designed to enhance online accuracy of Online Continual Learning (OCL) algorithms while dynamically adapting to varying memory budgets, positioning it as a premier solution for efficient and adaptive OCL framework in real-time environments.
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- Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training5
This work shows how simple and effective the fast recovery can be achieved at minute-level, in particular, a 35.9% accuracy spike over 3 epochs of training on Vim-Ti, and that a quick round of retraining after token merging yeilds robust results across various compression ratios.
- Personalized Federated Learning with Hidden Information on Personalized Prior4
Bayesian learning is employed to model PFL by assuming a prior in the scaled exponential family, and therefore pFedBreD, a framework to solve the problem the authors model using Bregman divergence regularization significantly outcompetes other PFL algorithms on multiple public benchmarks.
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- Correction to: FLSGD: free local SGD with parallel synchronization1
This paper presents a probabilistic procedure for estimating the number of nodes in a distributed supercomputing system using a simple, scalable, and scalable approaches called "cells".
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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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