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09/05/2023
Data-Juicer: A One-Stop Data Processing System for Large Language Models
The immense evolution in Large Language Models (LLMs) has underscored th...
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09/01/2023
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning
LLMs have demonstrated great capabilities in various NLP tasks. Differen...
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08/29/2023
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
Large language models (LLMs) have emerged as a new paradigm for Text-to-...
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07/16/2023
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study
Despite the superior performance, Large Language Models (LLMs) require s...
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05/04/2023
Efficient Personalized Federated Learning via Sparse Model-Adaptation
Federated Learning (FL) aims to train machine learning models for multip...
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03/23/2023
FS-Real: Towards Real-World Cross-Device Federated Learning
Federated Learning (FL) aims to train high-quality models in collaborati...
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11/09/2022
Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning
Choosing the values of hyper-parameters in sparse Bayesian learning (SBL...
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10/08/2022
Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks
In this paper, we investigate signal detection in multiple-input-multipl...
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06/08/2022
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning
Personalized Federated Learning (pFL), which utilizes and deploys distin...
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06/07/2022
Federated Hetero-Task Learning
To investigate the heterogeneity of federated learning in real-world sce...
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04/11/2022
FederatedScope: A Comprehensive and Flexible Federated Learning Platform via Message Passing
Although remarkable progress has been made by the existing federated lea...
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07/01/2020
Massive MIMO As Extreme Learning Machine
This work shows that massive multiple-input multiple-output (MIMO) with ...
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05/23/2019
Disentangling Redundancy for Multi-Task Pruning
Can prior network pruning strategies eliminate redundancy in multiple co...
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02/27/2019
