Search Results for author:
Found 9 papers, 2 papers with code
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents.
Flames: Benchmarking Value Alignment of LLMs in Chinese
The widespread adoption of large language models (LLMs) across various regions underscores the urgent need to evaluate their alignment with human values.
Fake Alignment: Are LLMs Really Aligned Well?
The growing awareness of safety concerns in large language models (LLMs) has sparked considerable interest in the evaluation of safety.
SFP: Spurious Feature-targeted Pruning for Out-of-Distribution Generalization
Based on the idea that in-distribution (ID) data with spurious features may have a lower experience risk, in this paper, we propose a novel Spurious Feature-targeted model Pruning framework, dubbed SFP, to automatically explore invariant substructures without referring to the above drawbacks.
Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models
Federated learning (FL) is an emerging technique that trains massive and geographically distributed edge data while maintaining privacy.
Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning
Mixed-precision quantization mostly predetermines the model bit-width settings before actual training due to the non-differential bit-width sampling process, obtaining sub-optimal performance.
Exploring Optimal Substructure for Out-of-distribution Generalization via Feature-targeted Model Pruning
Recent studies show that even highly biased dense networks contain an unbiased substructure that can achieve better out-of-distribution (OOD) generalization than the original model.
Efficient Stein Variational Inference for Reliable Distribution-lossless Network Pruning
Extensive experiments based on small Cifar-10 and large-scaled ImageNet demonstrate that our method can obtain sparser networks with great generalization performance while providing quantified reliability for the pruned model.
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning
We prove that FedFoA is a model-agnostic training framework and can be easily compatible with state-of-the-art unsupervised FL methods.
