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Low-rank adaptation~(LoRA) has recently gained much interest in fine-tuning foundation models.
Digital image processing
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On sparse reconstruction from fourier and gaussian measurements
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Randomized sampling and sparsity: Getting more information from fewer samples
Herrmann, F. J · 2010
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Gradient-based image recovery methods from incomplete fourier measurements
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Cats and dogs
Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C · 2012
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Spectral compressed sensing via structured matrix completion
Chen, Y. and Chi, Y · 2013
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Spectral compressive sensing
Duarte, M. F. and Baraniuk, R. G · 2013
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3d object representations for fine-grained categorization
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Fine-grained visual classification of aircraft
Maji, S., Rahtu, E., Kannala, J., Blaschko, M., and Vedaldi, A · 2013
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Magnetic resonance imaging: theory and practice
Vlaardingerbroek, M. T. and Boer, J. A · 2013
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Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A · 2014
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On the sample complexity of random fourier features for online learning: How many random fourier features do we need?
Lin, M., Weng, S., and Zhang, C · 2014
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Gespar: Efficient phase retrieval of sparse signals
Shechtman, Y., Beck, A., and Eldar, Y. C · 2014
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Light field reconstruction using sparsity in the continuous fourier domain
Shi, L., Hassanieh, H., Davis, A., Katabi, D., and Durand, F · 2014
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Exact joint sparse frequency recovery via optimization methods
Yang, Z. and Xie, L · 2016
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Remote sensing image scene classification: Benchmark and state of the art
Cheng, G., Han, J., and Lu, X · 2017
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The e2e dataset: New challenges for end-to-end generation
Novikova, J., Dušek, O., and Rieser, V · 2017
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Learning multiple visual domains with residual adapters
Rebuffi, S.-A., Bilen, H., and Vedaldi, A · 2017
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Faster neural networks straight from jpeg
Gueguen, L., Sergeev, A., Kadlec, B., Liu, R., and Yosinski, J · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2018
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Fourier reconstruction with sparse inversion
Zwartjes, P. and Gisolf, A · 2018
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Deep residual learning in the jpeg transform domain
Ehrlich, M. and Davis, L. S · 2019
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Helber, P., Bischke, B., Dengel, A., and Borth, D · 2019
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Parameter-efficient transfer learning for nlp
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S · 2019
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Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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Sampled softmax with random fourier features
Rawat, A. S., Chen, J., Yu, F. X. X., Suresh, A. T., and Kumar, S · 2019
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Solving inverse problems in medical imaging with score-based generative models
Song, Y., Shen, L., Xing, L., and Ermon, S · 2021
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Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2021
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Zaken, E. B., Ravfogel, S., and Goldberg, Y · 2021
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Black-box prompt learning for pre-trained language models
Diao, S., Huang, Z., Xu, R., Li, X., Lin, Y., Zhou, X., and Zhang, T · 2022
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Handling missing data via max-entropy regularized graph autoencoder
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
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Making pre-trained language models better few-shot learners
Gao, T., Fisch, A., and Chen, D · 2020
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Deberta: Decoding-enhanced bert with disentangled attention
He, P., Liu, X., Gao, J., and Chen, W · 2020
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Exploring versatile generative language model via parameter-efficient transfer learning
Lin, Z., Madotto, A., and Fung, P · 2020
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Adapterfusion: Non-destructive task composition for transfer learning
Pfeiffer, J., Kamath, A., Rücklé, A., Cho, K., and Gurevych, I · 2020
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Pre-trained models for natural language processing: A survey
Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., and Huang, X · 2020
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Gao, Z., Niu, Y., Cheng, J., Tang, J., Xu, T., Zhao, P., Li, L., Tsung, F., and Li, J · 2022
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Community question answering entity linking via leveraging auxiliary data
Li, Y., Shen, W., Gao, J., and Wang, Y · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Flava: A foundational language and vision alignment model
Singh, A., Hu, R., Goswami, V., Couairon, G., Galuba, W., Rohrbach, M., and Kiela, D · 2022
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Rethinking graph neural networks for anomaly detection
Tang, J., Li, J., Gao, Z., and Li, J · 2022
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Valipour, M., Rezagholizadeh, M., Kobyzev, I., and Ghodsi, A · 2022
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Self-instruct: Aligning language model with self generated instructions
Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., and Hajishirzi, H · 2022
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{ \{ PetS } \} : A unified framework for { \{ Parameter-Efficient } \} transformers serving
Zhou, Z., Wei, X., Zhang, J., and Sun, G · 2022
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., et al · 2023
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Qlora: Efficient finetuning of quantized llms
Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L · 2023
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., and Aberman, K · 2023
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Stanford alpaca: An instruction-following llama model, 2023
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
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Adaptive budget allocation for parameter-efficient fine-tuning
Zhang, Q., Chen, M., Bukharin, A., He, P., Cheng, Y., Chen, W., and Zhao, T · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al · 2023
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Graphwiz: An instruction-following language model for graph problems
Chen, N., Li, Y., Tang, J., and Li, J · 2024
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https://civitai.com/ , 2024
Civitai · 2024
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Protein multimer structure prediction via prompt learning
Gao, Z., Sun, X., Liu, Z., Li, Y., Cheng, H., and Li, J · 2024
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