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Model merging aims to cheaply combine individual task-specific models into a single multitask model.
Methods of conjugate gradients for solving linear systems 1
Magnus R. Hestenes and Eduard Stiefel · 1952
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Flat Minima
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Natural gradient works efficiently in learning
Shun-Ichi Amari · 1998
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The mnist database of handwritten digits
LeCun Yann · 1998
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini · 2005
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Reading digits in natural images with unsupervised feature learning
Netzer Yuval · 2011
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The winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern · 2012
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Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark
Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel · 2013
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Qualitatively characterizing neural network optimization problems
Ian J Goodfellow, Oriol Vinyals, and Andrew M Saxe · 2014
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Optimizing neural networks with kronecker-factored approximate curvature
James Martens and Roger Grosse · 2015
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A kronecker-factored approximate fisher matrix for convolution layers
Roger Grosse and James Martens · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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SUN database: Exploring a large collection of scene categories
Jianxiong Xiao, Krista A. Ehinger, James Hays, Antonio Torralba, and Aude Oliva · 2016
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Distributed second-order optimization using kronecker-factored approximations
Jimmy Ba, Roger Grosse, and James Martens · 2017
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Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman · 2017
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Wikipassageqa: A benchmark collection for research on non-factoid answer passage retrieval
Daniel Cohen, Liu Yang, and W Bruce Croft · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Essentially no barriers in neural network energy landscape
Felix Draxler, Kambis Veschgini, Manfred Salmhofer, and Fred Hamprecht · 2018
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Loss surfaces, mode connectivity, and fast ensembling of dnns
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, and Andrew G Wilson · 2018
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Fast approximate natural gradient descent in a kronecker factored eigenbasis
Thomas George, César Laurent, Xavier Bouthillier, Nicolas Ballas, and Pascal Vincent · 2018
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Convergence of the conjugate gradient method on singular systems
Ken Hayami · 2018
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Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2018
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Fisher information matrix
Agustinus Kristiadi · 2018
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Kronecker-factored curvature approximations for recurrent neural networks
James Martens, Jimmy Ba, and Matt Johnson · 2018
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Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks
Jason Phang, Thibault Févry, and Samuel R Bowman · 2018
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Tackling the story ending biases in the story cloze test
Rishi Sharma, James Allen, Omid Bakhshandeh, and Nasrin Mostafazadeh · 2018
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Local SGD converges fast and communicates little
Sebastian U. Stich · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
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Emergent properties of the local geometry of neural loss landscapes
Stanislav Fort and Surya Ganguli · 2019
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Large scale structure of neural network loss landscapes
Stanislav Fort and Stanislaw Jastrzebski · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Cosmos qa: Machine reading comprehension with contextual commonsense reasoning
Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2019
Kaisa: an adaptive second-order optimizer framework for deep neural networks
J Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian Foster, and Zhao Zhang · 2021
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Learning transferable visual models from natural language supervision
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
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al · 2021
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Skfac: Training neural networks with faster kronecker-factored approximate curvature
Zedong Tang, Fenlong Jiang, Maoguo Gong, Hao Li, Yue Wu, Fan Yu, Zidong Wang, and Min Wang · 2021
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Learning neural network subspaces
Mitchell Wortsman, Maxwell C Horton, Carlos Guestrin, Ali Farhadi, and Mohammad Rastegari · 2021
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Cited alongside, same era.
Explaining landscape connectivity of low-cost solutions for multilayer nets
Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Rong Ge, and Sanjeev Arora · 2019
Cited alongside, same era.
Limitations of the empirical fisher approximation for natural gradient descent
Frederik Kunstner, Philipp Hennig, and Lukas Balles · 2019
Cited alongside, same era.
Reasoning over paragraph effects in situations
Kevin Lin, Oyvind Tafjord, Peter Clark, and Matt Gardner · 2019
Cited alongside, same era.
A simple baseline for bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson · 2019
Cited alongside, same era.
Uniform convergence may be unable to explain generalization in deep learning
Vaishnavh Nagarajan and J Zico Kolter · 2019
Cited alongside, same era.
Socialiqa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, and Yejin Choi · 2019
Cited alongside, same era.
Quartz: An open-domain dataset of qualitative relationship questions
Oyvind Tafjord, Matt Gardner, Kevin Lin, and Peter Clark · 2019
Cited alongside, same era.
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Git re-basin: Merging models modulo permutation symmetries
Samuel K Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa · 2022
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Promptsource: An integrated development environment and repository for natural language prompts
Stephen H Bach, Victor Sanh, Zheng-Xin Yong, Albert Webson, Colin Raffel, Nihal V Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, et al · 2022
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Fusing finetuned models for better pretraining
Leshem Choshen, Elad Venezian, Noam Slonim, and Yoav Katz · 2022
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ColD fusion: Collaborative descent for distributed multitask finetuning
Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, and Leshem Choshen · 2022
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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 2022
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Dataless knowledge fusion by merging weights of language models
Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro, and Pengxiang Cheng · 2022
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Repair: Renormalizing permuted activations for interpolation repair
Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, and Behnam Neyshabur · 2022
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Linear connectivity reveals generalization strategies
Jeevesh Juneja, Rachit Bansal, Kyunghyun Cho, João Sedoc, and Naomi Saphra · 2022
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel · 2022
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Merging models with fisher-weighted averaging
Michael S Matena and Colin A Raffel · 2022
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Exploring mode connectivity for pre-trained language models
Yujia Qin, Cheng Qian, Jing Yi, Weize Chen, Yankai Lin, Xu Han, Zhiyuan Liu, Maosong Sun, and Jie Zhou · 2022
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Recycling diverse models for out-of-distribution generalization
Alexandre Ramé, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Léon Bottou, and David Lopez-Paz · 2022
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Pre-train your loss: Easy bayesian transfer learning with informative priors
Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, and Andrew G Wilson · 2022
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Not all tasks are born equal: Understanding zero-shot generalization
Jing Zhou, Zongyu Lin, Yanan Zheng, Jian Li, and Zhilin Yang · 2022
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Model merging by uncertainty-based gradient matching
Nico Daheim, Thomas Möllenhoff, Edoardo Maria Ponti, Iryna Gurevych, and Mohammad Emtiyaz Khan · 2023
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Studying large language model generalization with influence functions
Roger Grosse, Juhan Bae, Cem Anil, Nelson Elhage, Alex Tamkin, Amirhossein Tajdini, Benoit Steiner, Dustin Li, Esin Durmus, Ethan Perez, et al · 2023
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Knowledge is a region in weight space for fine-tuned language models
Almog Gueta, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, and Leshem Choshen · 2023
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Mechanistic mode connectivity
Ekdeep Singh Lubana, Eric J Bigelow, Robert P Dick, David Krueger, and Hidenori Tanaka · 2023
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Task arithmetic in the tangent space: Improved editing of pre-trained models
Guillermo Ortiz-Jimenez, Alessandro Favero, and Pascal Frossard · 2023
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Pipefisher: Efficient training of large language models using pipelining and fisher information matrices
Kazuki Osawa, Shigang Li, and Torsten Hoefler · 2023
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Re-basin via implicit sinkhorn differentiation
Fidel A Guerrero Peña, Heitor Rapela Medeiros, Thomas Dubail, Masih Aminbeidokhti, Eric Granger, and Marco Pedersoli · 2023
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Sdxl: improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2023
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Zipit! merging models from different tasks without training
George Stoica, Daniel Bolya, Jakob Bjorner, Taylor Hearn, and Judy Hoffman · 2023
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An empirical study of multimodal model merging
Yi-Lin Sung, Linjie Li, Kevin Lin, Zhe Gan, Mohit Bansal, and Lijuan Wang · 2023
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Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal · 2023
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