Fetching the paper…
Reading the bibliography…
Model merging is an emerging technique that integrates multiple models fine-tuned on different tasks to create a versatile model that excels in multiple domains.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al · 2011
Earlier work this paper cites.
The german traffic sign recognition benchmark: a multi-class classification competition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2011
Earlier work this paper cites.
The mnist database of handwritten digit images for machine learning research [best of the web]
Li Deng · 2012
Earlier work this paper cites.
Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Torchvision: Pytorch’s computer vision library
TorchVision maintainers and contributors · 2016
Earlier work this paper cites.
Convolutional neural networks for medical image analysis: Full training or fine tuning?
Nima Tajbakhsh, Jae Y Shin, Suryakanth R Gurudu, R Todd Hurst, Christopher B Kendall, Michael B Gotway, and Jianming Liang · 2016
Earlier work this paper cites.
t-sne-cuda: Gpu-accelerated t-sne and its applications to modern data
David M Chan, Roshan Rao, Forrest Huang, and John F Canny · 2018
Earlier work this paper cites.
Badnets: Identifying vulnerabilities in the machine learning model supply chain, 2019
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2019
Earlier work this paper cites.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
Earlier work this paper cites.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
Earlier work this paper cites.
Pytorch image models
Ross Wightman · 2019
Earlier work this paper cites.
Huggingface’s transformers: State-of-the-art natural language processing
T Wolf · 2019
Cited alongside, same era.
Pre-trained image processing transformer
Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao · 2021
Cited alongside, same era.
Pre-trained models: Past, present and future
Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Yuan Yao, Ao Zhang, Liang Zhang, et al · 2021
Cited alongside, same era.
On the effectiveness of adapter-based tuning for pretrained language model adaptation, 2021
Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jia-Wei Low, Lidong Bing, and Luo Si · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
An empirical study of multimodal model merging
Yi-Lin Sung, Linjie Li, Kevin Lin, Zhe Gan, Mohit Bansal, and Lijuan Wang · 2023
Later among the works it cites.
Large-scale multi-modal pre-trained models: A comprehensive survey
Xiao Wang, Guangyao Chen, Guangwu Qian, Pengcheng Gao, Xiao-Yong Wei, Yaowei Wang, Yonghong Tian, and Wen Gao · 2023
Later among the works it cites.
Ties-merging: Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal · 2023
Later among the works it cites.
Adamerging: Adaptive model merging for multi-task learning
Enneng Yang, Zhenyi Wang, Li Shen, Shiwei Liu, Guibing Guo, Xingwei Wang, and Dacheng Tao · 2023
Later among the works it cites.
https://modelzoo.co/
Model Zoo · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fedpara: Low-rank hadamard product for communication-efficient federated learning
Nam Hyeon-Woo, Moon Ye-Bin, and Tae-Hyun Oh · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning, 2021
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation, 2021
Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision, 2021
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
Cited alongside, same era.
A survey of vision-language pre-trained models
Yifan Du, Zikang Liu, Junyi Li, and Wayne Xin Zhao · 2022
Cited alongside, same era.
Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 2022
Cited alongside, same era.
Dataless knowledge fusion by merging weights of language models
Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro, and Pengxiang Cheng · 2022
Cited alongside, same era.
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2024
Closest in time.
Lora+: Efficient low rank adaptation of large models, 2024
Soufiane Hayou, Nikhil Ghosh, and Bin Yu · 2024
Closest in time.
Lora-as-an-attack! piercing llm safety under the share-and-play scenario, 2024
Hongyi Liu, Zirui Liu, Ruixiang Tang, Jiayi Yuan, Shaochen Zhong, Yu-Neng Chuang, Li Li, Rui Chen, and Xia Hu · 2024
Closest in time.
Full parameter fine-tuning for large language models with limited resources, 2024
Kai Lv, Yuqing Yang, Tengxiao Liu, Qinghui Gao, Qipeng Guo, and Xipeng Qiu · 2024
Closest in time.
Multiagent reinforcement learning for efficient cyber attack detection on the internet of medical things using tsne-zoa based dimensionality reduction
A Manikandan et al · 2024
Closest in time.
Exploring layerwise adversarial robustness through the lens of t-sne
Inês Valentim, Nuno Antunes, and Nuno Lourenço · 2024
Closest in time.
Ties-merging: Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin A Raffel, and Mohit Bansal · 2024
Closest in time.
Model merging in llms, mllms, and beyond: Methods, theories, applications and opportunities
Enneng Yang, Li Shen, Guibing Guo, Xingwei Wang, Xiaochun Cao, Jie Zhang, and Dacheng Tao · 2024
Closest in time.
Poisoning federated recommender systems with fake users, 2024
Ming Yin, Yichang Xu, Minghong Fang, and Neil Zhenqiang Gong · 2024
Closest in time.
Badmerging: Backdoor attacks against model merging
Jinghuai Zhang, Jianfeng Chi, Zheng Li, Kunlin Cai, Yang Zhang, and Yuan Tian · 2024
Closest in time.
Shaokun Zhang, Ming Yin, Jieyu Zhang, Jiale Liu, Zhiguang Han, Jingyang Zhang, Beibin Li, Chi Wang, Huazheng Wang, Yiran Chen, and Qingyun Wu · 2025
Closest in time.