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Training a unified model to take multiple targets into account is a trend towards artificial general intelligence.
A stochastic approximation method
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Multitask learning: A knowledge-based source of inductive bias1
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Indoor segmentation and support inference from rgbd images
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Adam: A method for stochastic optimization
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Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser
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Cgc: A flexible and robust approach to integrating co-regularized multi-domain graph for clustering
Cheng, W., Guo, Z., Zhang, X., and Wang, W · 2016
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The cityscapes dataset for semantic urban scene understanding
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Cross-stitch networks for multi-task learning
Misra, I., Shrivastava, A., Gupta, A., and Hebert, M · 2016
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Learning multiple tasks with multilinear relationship networks
Long, M., Cao, Z., Wang, J., and Yu, P. S · 2017
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
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Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification
Lu, Y., Kumar, A., Zhai, S., Cheng, Y., Javidi, T., and Feris, R · 2017
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An overview of multi-task learning in deep neural networks
Ruder, S · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., and Dean, J · 2017
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Deep hashing network for unsupervised domain adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., and Panchanathan, S · 2017
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Low-rank knowledge decomposition for medical foundation models
Zhou, Y., Li, H., Du, S., Yao, J., Zhang, Y., and Wang, Y · 2017
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Chen, Z., Badrinarayanan, V., Lee, C.-Y., and Rabinovich, A · 2018
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Kendall, A., Gal, Y., and Cipolla, R · 2018
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Domain generalization with adversarial feature learning
Li, H., Pan, S. J., Wang, S., and Kot, A. C · 2018
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Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., and Chi, E. H · 2018
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Incorporating prior domain knowledge into deep neural networks
Muralidhar, N., Islam, M. R., Marwah, M., Karpatne, A., and Ramakrishnan, N · 2018
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Multi-task learning as multi-objective optimization
Sener, O. and Koltun, V · 2018
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End-to-end multi-task learning with attention
Liu, S., Johns, E., and Davison, A. J · 2019
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Just pick a sign: Optimizing deep multitask models with gradient sign dropout
Chen, Z., Ngiam, J., Huang, Y., Luong, T., Kretzschmar, H., Chai, Y., and Anguelov, D · 2020
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Multi-task learning with deep neural networks: A survey
Crawshaw, M · 2020
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Learning to branch for multi-task learning
Guo, P., Lee, C.-Y., and Ulbricht, D · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
Radimagenet: an open radiologic deep learning research dataset for effective transfer learning
Mei, X., Liu, Z., Robson, P. M., Marinelli, B., Huang, M., Doshi, A., Jacobi, A., Cao, C., Link, K. E., Yang, T., et al · 2022
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Multi-task learning as a bargaining game
Navon, A., Shamsian, A., Achituve, I., Maron, H., Kawaguchi, K., Chechik, G., and Fetaya, E · 2022
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Simplified transfer learning for chest radiography models using less data
Sellergren, A. B., Chen, C., Nabulsi, Z., Li, Y., Maschinot, A., Sarna, A., Huang, J., Lau, C., Kalidindi, S. R., Etemadi, M., et al · 2022
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Task adaptive parameter sharing for multi-task learning
Wallingford, M., Li, H., Achille, A., Ravichandran, A., Fowlkes, C., Bhotika, R., and Soatto, S · 2022
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Decentralized training of foundation models in heterogeneous environments
Yuan, B., He, Y., Davis, J., Zhang, T., Dao, T., Chen, B., Liang, P. S., Re, C., and Zhang, C · 2022
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Adashare: Learning what to share for efficient deep multi-task learning
Sun, X., Panda, R., Feris, R., and Saenko, K · 2020
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Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations
Tang, H., Liu, J., Zhao, M., and Gong, X · 2020
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Wang, Z., Tsvetkov, Y., Firat, O., and Cao, Y · 2020
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Gradient surgery for multi-task learning
Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., and Finn, C · 2020
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Ms-kd: Multi-organ segmentation with multiple binary-labeled datasets
Feng, S., Zhou, Y., Zhang, X., Zhang, Y., and Wang, Y · 2021
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Dselect-k: Differentiable selection in the mixture of experts with applications to multi-task learning
Hazimeh, H., Zhao, Z., Chowdhery, A., Sathiamoorthy, M., Chen, Y., Mazumder, R., Hong, L., and Chi, E · 2021
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One-for-all: Generalized lora for parameter-efficient fine-tuning
Chavan, A., Liu, Z., Gupta, D., Xing, E., and Shen, Z · 2023
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Dou, S., Zhou, E., Liu, Y., Gao, S., Zhao, J., Shen, W., Zhou, Y., Xi, Z., Wang, X., Fan, X., et al · 2023
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Federated learning with bilateral curation for partially class-disjoint data
Fan, Z., Yao, J., Han, B., Zhang, Y., Wang, Y., et al · 2023
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Mixture of cluster-conditional lora experts for vision-language instruction tuning
Gou, Y., Liu, Z., Chen, K., Hong, L., Xu, H., Li, A., Yeung, D.-Y., Kwok, J. T., and Zhang, Y · 2023
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Lorahub: Efficient cross-task generalization via dynamic lora composition
Huang, C., Liu, Q., Lin, B. Y., Pang, T., Du, C., and Lin, M · 2023
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Loftq: Lora-fine-tuning-aware quantization for large language models
Li, Y., Yu, Y., Liang, C., He, P., Karampatziakis, N., Chen, W., and Zhao, T · 2023
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Lcm-lora: A universal stable-diffusion acceleration module
Luo, S., Tan, Y., Patil, S., Gu, D., von Platen, P., Passos, A., Huang, L., Li, J., and Zhao, H · 2023
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Independent component alignment for multi-task learning
Senushkin, D., Patakin, N., Kuznetsov, A., and Konushin, A · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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Edge-cloud polarization and collaboration: A comprehensive survey for ai
Yao, J., Zhang, S., Yao, Y., Wang, F., Ma, J., Zhang, J., Chu, Y., Ji, L., Jia, K., Shen, T., et al · 2023
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The expressive power of low-rank adaptation
Zeng, Y. and Lee, K · 2023
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Lora-fa: Memory-efficient low-rank adaptation for large language models fine-tuning
Zhang, L., Zhang, L., Shi, S., Chu, X., and Li, B · 2023
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Fan, Z., Hu, S., Yao, J., Niu, G., Zhang, Y., Sugiyama, M., and Wang, Y · 2024
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Harmodt: Harmony multi-task decision transformer for offline reinforcement learning
Hu, S., Fan, Z., Shen, L., Zhang, Y., Wang, Y., and Tao, D · 2024
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Domain-inspired sharpness-aware minimization under domain shifts
Zhang, R., Fan, Z., Yao, J., Zhang, Y., and Wang, Y · 2024
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