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Found 18 papers, 9 papers with code
Less is More: Data Value Estimation for Visual Instruction Tuning
To investigate this issue, we conduct a series of empirical studies, which reveal a significant redundancy within the visual instruction datasets, and show that greatly reducing the amount of several instruction dataset even do not affect the performance.
AgentScope: A Flexible yet Robust Multi-Agent Platform
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications.
Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning
Recently, multi-task instruction tuning has been applied into sentence representation learning, which endows the capability of generating specific representations with the guidance of task instruction, exhibiting strong generalization ability on new tasks.
Data-Juicer: A One-Stop Data Processing System for Large Language Models
A data recipe is a mixture of data from different sources for training LLMs, which plays a vital role in LLMs' performance.
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning
When several entities have similar interested tasks, but their data cannot be shared because of privacy concerns regulations, federated learning (FL) is a mainstream solution to leverage the data of different entities.
Message Passing Based Block Sparse Signal Recovery for DOA Estimation Using Large Arrays
This work deals with directional of arrival (DOA) estimation with a large antenna array.
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
Our explorations highlight open-source LLMs' potential in Text-to-SQL, as well as the advantages and disadvantages of the supervised fine-tuning.
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Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study
Different from previous studies focused on overall performance, this work aims to investigate the impact of quantization on \emph{emergent abilities}, which are important characteristics that distinguish LLMs from small language models.
Efficient Personalized Federated Learning via Sparse Model-Adaptation
To overcome these challenges, we propose a novel approach named pFedGate for efficient personalized FL by adaptively and efficiently learning sparse local models.
FS-Real: Towards Real-World Cross-Device Federated Learning
Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data, which attracts increasing attention in both academia and industry.
Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning
Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance.
Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks
Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals.
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning
Personalized Federated Learning (pFL), which utilizes and deploys distinct local models, has gained increasing attention in recent years due to its success in handling the statistical heterogeneity of FL clients.
A Benchmark for Federated Hetero-Task Learning
To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of both data distribution and learning tasks.
FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challenges brought by various types of heterogeneity, including the heterogeneity in participants' local data, resources, behaviors and learning goals.
Massive MIMO As an Extreme Learning Machine
This work shows that a massive multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs) forms a natural extreme learning machine (ELM).
Pruning-Aware Merging for Efficient Multitask Inference
Given a set of deep neural networks, each pre-trained for a single task, it is desired that executing arbitrary combinations of tasks yields minimal computation cost.
Extreme Learning Machine-Based Receiver for MIMO LED Communications
This work concerns receiver design for light-emitting diode (LED) multiple input multiple output (MIMO) communications where the LED nonlinearity can severely degrade the performance of communications.
