Search Results for author: Fangcheng Fu

Found 10 papers, 3 papers with code

Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript

no code implementations ICML 2020 Fangcheng Fu, Yuzheng Hu, Yihan He, Jiawei Jiang, Yingxia Shao, Ce Zhang, Bin Cui

Recent years have witnessed intensive research interests on training deep neural networks (DNNs) more efficiently by quantization-based compression methods, which facilitate DNNs training in two ways: (1) activations are quantized to shrink the memory consumption, and (2) gradients are quantized to decrease the communication cost.

Quantization

Retrieval-Augmented Generation for AI-Generated Content: A Survey

1 code implementation29 Feb 2024 Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, Bin Cui

The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by advancements in model algorithms, the increasing scale of foundation models, and the availability of ample high-quality datasets.

Information Retrieval Large Language Model +2

Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning

no code implementations24 Oct 2023 Yuxiang Wang, Xiao Yan, Chuang Hu, Fangcheng Fu, Wentao Zhang, Hao Wang, Shuo Shang, Jiawei Jiang

For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features.

Contrastive Learning Graph Classification +4

Improving Automatic Parallel Training via Balanced Memory Workload Optimization

1 code implementation5 Jul 2023 Yujie Wang, Youhe Jiang, Xupeng Miao, Fangcheng Fu, Shenhan Zhu, Xiaonan Nie, Yaofeng Tu, Bin Cui

Transformer models have emerged as the leading approach for achieving state-of-the-art performance across various application domains, serving as the foundation for advanced large-scale deep learning (DL) models.

Navigate

Accelerating Text-to-Image Editing via Cache-Enabled Sparse Diffusion Inference

1 code implementation27 May 2023 Zihao Yu, Haoyang Li, Fangcheng Fu, Xupeng Miao, Bin Cui

The key intuition behind our approach is to utilize the semantic mapping between the minor modifications on the input text and the affected regions on the output image.

Text-to-Image Generation

Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent

no code implementations6 Mar 2023 Xiaonan Nie, Yi Liu, Fangcheng Fu, Jinbao Xue, Dian Jiao, Xupeng Miao, Yangyu Tao, Bin Cui

Recent years have witnessed the unprecedented achievements of large-scale pre-trained models, especially the Transformer models.

Management Scheduling

Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates

no code implementations29 Jul 2022 Fangcheng Fu, Xupeng Miao, Jiawei Jiang, Huanran Xue, Bin Cui

Vertical federated learning (VFL) is an emerging paradigm that allows different parties (e. g., organizations or enterprises) to collaboratively build machine learning models with privacy protection.

Vertical Federated Learning

BlindFL: Vertical Federated Machine Learning without Peeking into Your Data

no code implementations16 Jun 2022 Fangcheng Fu, Huanran Xue, Yong Cheng, Yangyu Tao, Bin Cui

First, to address the functionality of VFL models, we propose the federated source layers to unite the data from different parties.

BIG-bench Machine Learning Vertical Federated Learning

K-Core Decomposition on Super Large Graphs with Limited Resources

no code implementations26 Dec 2021 Shicheng Gao, Jie Xu, Xiaosen Li, Fangcheng Fu, Wentao Zhang, Wen Ouyang, Yangyu Tao, Bin Cui

For example, the distributed K-core decomposition algorithm can scale to a large graph with 136 billion edges without losing correctness with our divide-and-conquer technique.

An Experimental Evaluation of Large Scale GBDT Systems

no code implementations3 Jul 2019 Fangcheng Fu, Jiawei Jiang, Yingxia Shao, Bin Cui

Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications.

Management

Cannot find the paper you are looking for? You can Submit a new open access paper.