Search Results for author: Guihai Chen

Found 33 papers, 12 papers with code

2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution

1 code implementation10 Jun 2024 Kai Liu, Haotong Qin, Yong Guo, Xin Yuan, Linghe Kong, Guihai Chen, Yulun Zhang

Low-bit quantization has become widespread for compressing image super-resolution (SR) models for edge deployment, which allows advanced SR models to enjoy compact low-bit parameters and efficient integer/bitwise constructions for storage compression and inference acceleration, respectively.

Image Super-Resolution Quantization

Image Super-Resolution with Text Prompt Diffusion

1 code implementation24 Nov 2023 Zheng Chen, Yulun Zhang, Jinjin Gu, Xin Yuan, Linghe Kong, Guihai Chen, Xiaokang Yang

Specifically, we first design a text-image generation pipeline to integrate text into the SR dataset through the text degradation representation and degradation model.

Image Generation Image Super-Resolution +1

ECLM: Efficient Edge-Cloud Collaborative Learning with Continuous Environment Adaptation

no code implementations18 Nov 2023 Yan Zhuang, Zhenzhe Zheng, Yunfeng Shao, Bingshuai Li, Fan Wu, Guihai Chen

In this paper, we propose ECLM, an edge-cloud collaborative learning framework for rapid model adaptation for dynamic edge environments.

Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster

1 code implementation14 Nov 2023 Hongxuan Zhang, Zhining Liu, Yao Zhao, Jiaqi Zheng, Chenyi Zhuang, Jinjie Gu, Guihai Chen

In this work, we propose FastCoT, a model-agnostic framework based on parallel decoding without any further training of an auxiliary model or modification to the LLM itself.


Temporal Interest Network for User Response Prediction

1 code implementation15 Aug 2023 Haolin Zhou, Junwei Pan, Xinyi Zhou, Xihua Chen, Jie Jiang, Xiaofeng Gao, Guihai Chen

To fill this gap, we propose a Temporal Interest Network (TIN) to capture the semantic-temporal correlation simultaneously between behaviors and the target.

Click-Through Rate Prediction Recommendation Systems

DC-CCL: Device-Cloud Collaborative Controlled Learning for Large Vision Models

no code implementations18 Mar 2023 Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, Guihai Chen

In this work, we propose a device-cloud collaborative controlled learning framework, called DC-CCL, enabling a cloud-side large vision model that cannot be directly deployed on the mobile device to still benefit from the device-side local samples.

Knowledge Distillation

One-Time Model Adaptation to Heterogeneous Clients: An Intra-Client and Inter-Image Attention Design

no code implementations11 Nov 2022 Yikai Yan, Chaoyue Niu, Fan Wu, Qinya Li, Shaojie Tang, Chengfei Lyu, Guihai Chen

The mainstream workflow of image recognition applications is first training one global model on the cloud for a wide range of classes and then serving numerous clients, each with heterogeneous images from a small subset of classes to be recognized.

AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling

no code implementations27 Oct 2022 Zuowu Zheng, Xiaofeng Gao, Junwei Pan, Qi Luo, Guihai Chen, Dapeng Liu, Jie Jiang

In this paper, we propose a novel model named AutoAttention, which includes all item/user/context side fields as the query, and assigns a learnable weight for each field pair between behavior fields and query fields.

Click-Through Rate Prediction

On-Device Model Fine-Tuning with Label Correction in Recommender Systems

no code implementations21 Oct 2022 Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, Guihai Chen

To meet the practical requirements of low latency, low cost, and good privacy in online intelligent services, more and more deep learning models are offloaded from the cloud to mobile devices.

Click-Through Rate Prediction Recommendation Systems

To Store or Not? Online Data Selection for Federated Learning with Limited Storage

no code implementations1 Sep 2022 Chen Gong, Zhenzhe Zheng, Yunfeng Shao, Bingshuai Li, Fan Wu, Guihai Chen

We first define a new data valuation metric for data evaluation and selection in FL with theoretical guarantees for speeding up model convergence and enhancing final model accuracy, simultaneously.

Data Valuation Federated Learning +4

HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction

no code implementations1 Jun 2022 Zuowu Zheng, Changwang Zhang, Xiaofeng Gao, Guihai Chen

Based on this observation, in this paper, we propose a novel approach Hierarchical Intention Embedding Network (HIEN), which considers dependencies of attributes based on bottom-up tree aggregation in the constructed attribute graph.

Attribute Click-Through Rate Prediction +1

Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning

no code implementations30 May 2022 Chengfei Lv, Chaoyue Niu, Renjie Gu, Xiaotang Jiang, Zhaode Wang, Bin Liu, Ziqi Wu, Qiulin Yao, Congyu Huang, Panos Huang, Tao Huang, Hui Shu, Jinde Song, Bin Zou, Peng Lan, Guohuan Xu, Fei Wu, Shaojie Tang, Fan Wu, Guihai Chen

Walle consists of a deployment platform, distributing ML tasks to billion-scale devices in time; a data pipeline, efficiently preparing task input; and a compute container, providing a cross-platform and high-performance execution environment, while facilitating daily task iteration.

Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach

no code implementations25 Apr 2022 Chenxiao Yang, Qitian Wu, Jipeng Jin, Xiaofeng Gao, Junwei Pan, Guihai Chen

To circumvent false negatives, we develop a principled approach to improve the reliability of negative instances and prove that the objective is an unbiased estimation of sampling from the true negative distribution.

Collaborative Filtering

Cross-Task Knowledge Distillation in Multi-Task Recommendation

no code implementations20 Feb 2022 Chenxiao Yang, Junwei Pan, Xiaofeng Gao, Tingyu Jiang, Dapeng Liu, Guihai Chen

Multi-task learning (MTL) has been widely used in recommender systems, wherein predicting each type of user feedback on items (e. g, click, purchase) are treated as individual tasks and jointly trained with a unified model.

Knowledge Distillation Multi-Task Learning +1

Vertical Federated Learning: Challenges, Methodologies and Experiments

no code implementations9 Feb 2022 Kang Wei, Jun Li, Chuan Ma, Ming Ding, Sha Wei, Fan Wu, Guihai Chen, Thilina Ranbaduge

As a special architecture in FL, vertical FL (VFL) is capable of constructing a hyper ML model by embracing sub-models from different clients.

Vertical Federated Learning

On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systems

no code implementations24 Jan 2022 Renjie Gu, Chaoyue Niu, Yikai Yan, Fan Wu, Shaojie Tang, Rongfeng Jia, Chengfei Lyu, Guihai Chen

Data heterogeneity is an intrinsic property of recommender systems, making models trained over the global data on the cloud, which is the mainstream in industry, non-optimal to each individual user's local data distribution.

Data Augmentation Recommendation Systems

Federated Submodel Optimization for Hot and Cold Data Features

1 code implementation16 Sep 2021 Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lv, Yanghe Feng, Guihai Chen

We theoretically proved the convergence rate of FedSubAvg by deriving an upper bound under a new metric called the element-wise gradient norm.

Federated Learning

Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising

no code implementations7 Jun 2021 Xiangyu Liu, Chuan Yu, Zhilin Zhang, Zhenzhe Zheng, Yu Rong, Hongtao Lv, Da Huo, YiQing Wang, Dagui Chen, Jian Xu, Fan Wu, Guihai Chen, Xiaoqiang Zhu

In e-commerce advertising, it is crucial to jointly consider various performance metrics, e. g., user experience, advertiser utility, and platform revenue.

Toward Understanding the Influence of Individual Clients in Federated Learning

no code implementations20 Dec 2020 Yihao Xue, Chaoyue Niu, Zhenzhe Zheng, Shaojie Tang, Chengfei Lv, Fan Wu, Guihai Chen

Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server.

Federated Learning

Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability

1 code implementation18 Feb 2020 Yikai Yan, Chaoyue Niu, Yucheng Ding, Zhenzhe Zheng, Fan Wu, Guihai Chen, Shaojie Tang, Zhihua Wu

In this work, we consider a practical and ubiquitous issue when deploying federated learning in mobile environments: intermittent client availability, where the set of eligible clients may change during the training process.

Benchmarking Federated Learning

MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding

no code implementations18 Feb 2020 Haolin Zhou, Chaoqi Yang, Xiaofeng Gao, Qiong Chen, Gongshen Liu, Guihai Chen

Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs.

Reinforcement Learning (RL)

Online Pricing with Reserve Price Constraint for Personal Data Markets

1 code implementation28 Nov 2019 Chaoyue Niu, Zhenzhe Zheng, Fan Wu, Shaojie Tang, Guihai Chen

The analysis and evaluation results reveal that our proposed pricing mechanism incurs low practical regret, online latency, and memory overhead, and also demonstrate that the existence of reserve price can mitigate the cold-start problem in a posted price mechanism, and thus can reduce the cumulative regret.

A Hierarchical Optimizer for Recommendation System Based on Shortest Path Algorithm

no code implementations7 Nov 2019 Jiacheng Dai, Zhifeng Jia, Xiaofeng Gao, Guihai Chen

Top-k Nearest Geosocial Keyword (T-kNGK) query on geosocial network is defined to give users k recommendations based on some keywords and designated spatial range, and can be realized by shortest path algorithms.

Secure Federated Submodel Learning

1 code implementation6 Nov 2019 Chaoyue Niu, Fan Wu, Shaojie Tang, Lifeng Hua, Rongfei Jia, Chengfei Lv, Zhihua Wu, Guihai Chen

Nevertheless, the "position" of a client's truly required submodel corresponds to her private data, and its disclosure to the cloud server during interactions inevitably breaks the tenet of federated learning.

Federated Learning Position

From Server-Based to Client-Based Machine Learning: A Comprehensive Survey

no code implementations18 Sep 2019 Renjie Gu, Chaoyue Niu, Fan Wu, Guihai Chen, Chun Hu, Chengfei Lyu, Zhihua Wu

Another benefit is the bandwidth reduction because various kinds of local data can be involved in the training process without being uploaded.

BIG-bench Machine Learning

NETR-Tree: An Eifficient Framework for Social-Based Time-Aware Spatial Keyword Query

no code implementations26 Aug 2019 Xiuqi Huang, Yuanning Gao, Xiaofeng Gao, Guihai Chen

In the user layer, we exploit the network embedding strategy to measure the relationship effect in users' relationship network.

Network Embedding

Accelerate RNN-based Training with Importance Sampling

no code implementations31 Oct 2017 Fei Wang, Xiaofeng Gao, Guihai Chen, Jun Ye

Unfortunately, the calculation of the sampling probability distribution $P$ causes a major limitation of IS: it requires the input data to be well-structured, i. e., the feature vector is properly defined.

Stochastic Optimization

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