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Bolin Ding

As seen in: arXiv, MDPI
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Articles by Bolin Ding

BridgeScope: A Universal Toolkit for Bridging Large Language Models and Databases

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By Dandan Liu, Rong Zhu, Bolin Ding, Jingren Zhou
| arXiv
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Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model

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By Tian Zhou, Jinyang Gao, Bolin Ding, Jingren Zhou
| arXiv
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On the Pseudospectral Method for Solving the Fractional Klein-Gordon Equation Using Legendre Cardinal Functions

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By Tao Liu, Bolin Ding, Behzad Nemati Saray, Davron Aslonqulovich Juraev
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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A Novel and Effective Scheme for Solving the Fractional Telegraph Problem via the Spectral Element Method

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By Tao Liu, Bolin Ding, Runqi Xue, Davron A. Juraev
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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[2410.02189] Agent-Oriented Planning in Multi-Agent Systems

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By Yuexiang Xie, Songze Li, Fugee Tsung, Bolin Ding
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2408.15600] Exploring Selective Layer Fine-Tuning in Federated Learning

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By Yuexiang Xie, Bolin Ding, Yaliang Li, Jun Zhang
| arXiv
The Accessibility Forum starts next week! Sign up and join us in September.
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AAA: an Adaptive Mechanism for Locally Differential Private Mean Estimation

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By Xiaokui Xiao, Yin Yang, Bolin Ding, Ergute Bao
| arXiv
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EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models

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By Yanxi Chen, Yaliang Li, Bolin Ding, Jingren Zhou
| arXiv
arXiv:2402.00518 (cs) Download PDF Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2402.00518 [cs.LG] (or arXiv:2402.00518v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2402.00518 Submission history From: Yanxi Chen [ view email] [v1] Thu, 1 Feb 2024 11:39:04 UTC (1,413 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite...
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CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting

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By Tian Zhou, Qingsong Wen, Jinyang Gao, Bolin Ding
| arXiv
[Submitted on 20 May 2023 ( v1 ), last revised 16 Feb 2024 (this version, v5)] Title:CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting Download a PDF of the paper titled CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting, by Wang Xue and 5 other authors Download PDF HTML (experimental) Abstract:Recent studies have demonstrated the great power of Transformer models for time series forecasting.
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[2305.02776] Efficient Personalized Federated Learning via Sparse Model-Adaptation

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By Liuyi Yao, Dawei Gao, Bolin Ding, Yaliang Li
| arXiv
[Submitted on 4 May 2023 ( v1 ), last revised 9 Jun 2023 (this version, v2)] Title:Efficient Personalized Federated Learning via Sparse Model-Adaptation Download a PDF of the paper titled Efficient Personalized Federated Learning via Sparse Model-Adaptation, by Daoyuan Chen and 4 other authors Download PDF Abstract: Federated Learning (FL) aims to train machine learning models for multiple clients without sharing their own private data.
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Collecting telemetry data privately - Microsoft Research

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By Bolin Ding
| microsoft.com
The collection and analysis of telemetry data from users and their devices leads to improved user experiences and informed business decisions. However, users have concerns about their data privacy, including what personal information software and internet companies are gathering and whether their data is protected from potential leaks and hacks. Differential privacy (Dwork, et al. 2006) has emerged as the de facto standard for privacy guarantees.
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