Výsledky vyhledávání

Report

Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments

Autor: Jiang, Ziyang, Calhoun, Zach, Liu, Yiling, Duan, Lei, Carlson, David

Causal reasoning is often challenging with spatial data, particularly when handling high-dimensional inputs. To address this, we propose a neural network (NN) based framework integrated with an approximate Gaussian process to manage spatial interfere

Externí odkaz: http://arxiv.org/abs/2412.04285

Zobrazit plný text záznamu

Report

SCOP: A Sequence-Structure Contrast-Aware Framework for Protein Function Prediction

Autor: Ma, Runze, He, Chengxin, Zheng, Huiru, Wang, Xinye, Wang, Haiying, Zhang, Yidan, Duan, Lei

Improving the ability to predict protein function can potentially facilitate research in the fields of drug discovery and precision medicine. Technically, the properties of proteins are directly or indirectly reflected in their sequence and structure

Externí odkaz: http://arxiv.org/abs/2411.11366

Zobrazit plný text záznamu

Report

Fast Asymmetric Factorization for Large Scale Multiple Kernel Clustering

Autor: Chen, Yan, Du, Liang, Duan, Lei

Kernel methods are extensively employed for nonlinear data clustering, yet their effectiveness heavily relies on selecting suitable kernels and associated parameters, posing challenges in advance determination. In response, Multiple Kernel Clustering

Externí odkaz: http://arxiv.org/abs/2405.16447

Zobrazit plný text záznamu

Report

MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts

Autor: Li, Dengchun, Ma, Yingzi, Wang, Naizheng, Ye, Zhengmao, Cheng, Zhiyuan, Tang, Yinghao, Zhang, Yan, Duan, Lei, Zuo, Jie, Yang, Cal, Tang, Mingjie

Fine-tuning Large Language Models (LLMs) is a common practice to adapt pre-trained models for specific applications. While methods like LoRA have effectively addressed GPU memory constraints during fine-tuning, their performance often falls short, es

Externí odkaz: http://arxiv.org/abs/2404.15159

Zobrazit plný text záznamu

Report

mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs

Autor: Ye, Zhengmao, Li, Dengchun, Hu, Zetao, Lan, Tingfeng, Sha, Jian, Zhang, Sicong, Duan, Lei, Zuo, Jie, Lu, Hui, Zhou, Yuanchun, Tang, Mingjie

Transformer-based, pre-trained large language models (LLMs) have demonstrated outstanding performance across diverse domains, particularly in the emerging {\em pretrain-then-finetune} paradigm. Low-Rank Adaptation (LoRA), a parameter-efficient fine-t

Externí odkaz: http://arxiv.org/abs/2312.02515

Zobrazit plný text záznamu