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Found 5 papers, 3 papers with code
MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA based Mixture of Experts
Unlike other LoRA based MoE methods, MixLoRA enhances model performance by utilizing independently configurable attention-layer LoRA adapters, supporting the use of LoRA and its variants for the construction of experts, and applying auxiliary load balance loss to address the imbalance problem of the router.
Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation
We show that the proposed data augmentation strategy helps enhance the performance of the state-of-the-art convolutional neural network-random forest (CNN-RF) model by a reasonable amount, resulting in a noteworthy improvement in spatial correlation and a reduction in prediction error.
ASPEN: High-Throughput LoRA Fine-Tuning of Large Language Models with a Single GPU
Transformer-based large language models (LLMs) have demonstrated outstanding performance across diverse domains, particularly when fine-turned for specific domains.
Probing the superconducting gap structure in the noncentrosymmetric topological superconductor ZrRuAs
The superconducting gap structure of a topological crystalline insulator (TCI) candidate ZrRuAs ($T^{\rm on}_{\rm c}$ = 7. 9(1) K) with a noncentrosymmetric crystal structure has been investigated using muon spin rotation/relaxation ($\mu$SR) measurements in transverse-field (TF) and zero-field (ZF) geometries.
Superconductivity Strongly Correlated Electrons
Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos
In recent years, heatmap regression based models have shown their effectiveness in face alignment and pose estimation.
