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Scaling full finetuning of large foundation models strains GPU memory and training time.
SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia · 2001
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Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett · 2005
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Synthesis of quantum-logic circuits
V.V. Shende, S.S. Bullock, and I.L. Markov · 2005
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini · 2006
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Learning multiple layers of features from tiny images
A Krizhevsky · 2009
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts · 2013
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Tensorizing Neural Networks, December 2015
Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, and Dmitry Vetrov · 2015
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Charles Beattie, Joel Z Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, et al · 2016
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Supervised Learning with Tensor Networks
Edwin Stoudenmire and David J Schwab · 2016
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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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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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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Learning towards minimum hyperspherical energy
Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, and Le Song · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang · 2018
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DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Neural network acceptability judgments
A Warstadt · 2019
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A large-scale study of representation learning with the visual task adaptation benchmark
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, et al · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy · 2020
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Language models are few-shot learners
Brown et al · 2020
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Pengcheng He, Jianfeng Gao, and Weizhu Chen · 2021
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Controlling text-to-image diffusion by orthogonal finetuning
Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, and Bernhard Schölkopf · 2023
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Adaptive budget allocation for parameter-efficient fine-tuning
Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, and Tuo Zhao · 2023
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QuanTA: Efficient high-rank fine-tuning of LLMs with quantum-informed tensor adaptation
Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen M Dugan, Di Luo, and Marin Soljacic · 2024
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Quantum vision transformers
El Amine Cherrat, Iordanis Kerenidis, Natansh Mathur, Jonas Landman, Martin Strahm, and Yun Yvonna Li · 2024
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Training-efficient density quantum machine learning
Brian Coyle, El Amine Cherrat, Nishant Jain, Natansh Mathur, Snehal Raj, Skander Kazdaghli, and Iordanis Kerenidis · 2024
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Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Nearest centroid classification on a trapped ion quantum computer
Sonika Johri, Shantanu Debnath, Abhinav Mocherla, et al · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Quantum unary approach to option pricing
Sergi Ramos-Calderer, Adrián Pérez-Salinas, Diego García-Martín, Carlos Bravo-Prieto, Jorge Cortada, Jordi Planagumà, and José I. Latorre · 2021
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BitFit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ben Zaken, Yoav Goldberg, and Shauli Ravfogel · 2022
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Quantum machine learning with subspace states
Iordanis Kerenidis and Anupam Prakash · 2022
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Quantum Methods for Neural Networks and Application to Medical Image Classification
Jonas Landman, Natansh Mathur, Yun Yvonna Li, Martin Strahm, Skander Kazdaghli, Anupam Prakash, and Iordanis Kerenidis · 2022
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Quantum Deep Hedging
El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky, and Marco Pistoia · 2023
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Renato Farias, Thiago O Maciel, Giancarlo Camilo, Ruge Lin, Sergi Ramos-Calderer, and Leandro Aolita · 2024
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VeRA: Vector-based random matrix adaptation
Dawid Jan Kopiczko, Tijmen Blankevoort, and Yuki M Asano · 2024
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Dora: weight-decomposed low-rank adaptation
Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Kwang-Ting Cheng, and Min-Hung Chen · 2024
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Léo Monbroussou, Eliott Z. Mamon, Jonas Landman, Alex B. Grilo, Romain Kukla, and Elham Kashefi · 2024
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Improved financial forecasting via quantum machine learning
Sohum Thakkar, Skander Kazdaghli, Natansh Mathur, Iordanis Kerenidis, André J. Ferreira–Martins, and Samurai Brito · 2024
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Andrei Tomut, Saeed S. Jahromi, Abhijoy Sarkar, Uygar Kurt, Sukhbinder Singh, Faysal Ishtiaq, Cesar Muñoz, Prabdeep Singh Bajaj, Ali Elborady, Gianni del Bimbo, Mehrazin Alizadeh, David Montero, Pablo Martin-Ramiro, Muhammad Ibrahim, Oussama Tahiri Alaoui, John Malcolm, Samuel Mugel, and Roman Orus · 2024
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Navigating text-to-image customization: From lyCORIS fine-tuning to model evaluation
SHIH-YING YEH, Yu-Guan Hsieh, Zhidong Gao, Bernard B W Yang, Giyeong Oh, and Yanmin Gong · 2024
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A Quantum Circuit-Based Compression Perspective for Parameter-Efficient Learning
Chen-Yu Liu, Chao-Han Huck Yang, Hsi-Sheng Goan, and Min-Hsiu Hsieh · 2025
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Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection, April 2025
Natansh Mathur, Brian Coyle, Nishant Jain, Snehal Raj, Akshat Tandon, Jasper Simon Krauser, and Rainer Stoessel · 2025
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Synergistic pretraining of parametrized quantum circuits via tensor networks
Manuel S. Rudolph, Jacob Miller, Danial Motlagh, Jing Chen, Atithi Acharya, and Alejandro Perdomo-Ortiz · 2041
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Matrix product state pre-training for quantum machine learning
James Dborin, Fergus Barratt, Vinul Wimalaweera, Lewis Wright, and Andrew G Green · 2058
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Subspace preserving quantum convolutional neural network architectures
Léo Monbroussou, Jonas Landman, Letao Wang, Alex B Grilo, and Elham Kashefi · 2058
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