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Deep neural networks have become foundational to advancements in multiple domains, including recommendation systems, natural language processing, and so on.
Language models are few-shot learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 1901
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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 · 1907
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Learning both weights and connections for efficient neural network
Han, S.; Pool, J.; Tran, J.; and Dally, W. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Session-based recommendations with recurrent neural networks
Hidasi, B.; Karatzoglou, A.; Baltrunas, L.; and Tikk, D. 2016 · 2016
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and¡ 0.5 MB model size
Iandola, F. N.; Han, S.; Moskewicz, M. W.; Ashraf, K.; Dally, W. J.; and Keutzer, K. 2016 · 2016
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Learning to prune deep neural networks via layer-wise optimal brain surgeon
Dong, X.; Chen, S.; and Pan, S. 2017 · 2017
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DeepFM: a factorization-machine based neural network for CTR prediction
Guo, H.; Tang, R.; Ye, Y.; Li, Z.; and He, X. 2017 · 2017
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; and Adam, H. 2017 · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
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Self-attentive sequential recommendation
Kang, W.-C.; and McAuley, J. 2018 · 2018
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Improving language understanding by generative pre-training
Radford, A.; Narasimhan, K.; Salimans, T.; Sutskever, I.; et al. 2018 · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; and Chen, L.-C. 2018 · 2018
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Deep interest network for click-through rate prediction
Zhou, G.; Zhu, X.; Song, C.; Fan, Y.; Zhu, H.; Ma, X.; Yan, Y.; Jin, J.; Li, H.; and Gai, K. 2018 · 2018
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Searching for mobilenetv3
Howard, A.; Sandler, M.; Chu, G.; Chen, L.-C.; Chen, B.; Tan, M.; Wang, W.; Zhu, Y.; Pang, R.; Vasudevan, V.; et al. 2019 · 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 · 2019
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BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer
Sun, F.; Liu, J.; Wu, J.; Pei, C.; Lin, X.; Ou, W.; and Jiang, P. 2019 · 2019
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A survey on ensemble learning
Dong, X.; Yu, Z.; Cao, W.; Shi, Y.; and Ma, Q. 2020 · 2020
Cited alongside, same era.
Linear mode connectivity and the lottery ticket hypothesis
Frankle, J.; Dziugaite, G. K.; Roy, D.; and Carbin, M. 2020 · 2020
Cited alongside, same era.
Multi-dimensional pruning: A unified framework for model compression
Guo, J.; Ouyang, W.; and Xu, D. 2020 · 2020
Cited alongside, same era.
Lightgcn: Simplifying and powering graph convolution network for recommendation
He, X.; Deng, K.; Wang, X.; Li, Y.; Zhang, Y.; and Wang, M. 2020 · 2020
Cited alongside, same era.
Movement pruning: Adaptive sparsity by fine-tuning
Sanh, V.; Wolf, T.; and Rush, A. 2020 · 2020
Cited alongside, same era.
Pruning from scratch
Wang, Y.; Zhang, X.; Xie, L.; Zhou, J.; Su, H.; Zhang, B.; and Hu, X. 2020 · 2020
Cited alongside, same era.
Fairfed: Enabling group fairness in federated learning
Ezzeldin, Y. H.; Yan, S.; He, C.; Ferrara, E.; and Avestimehr, A. S. 2023 · 2023
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The Lottery Ticket Hypothesis: On Sparse, Trainable Neural Networks
Frankle, J. 2023 · 2023
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Rethinking federated learning with domain shift: A prototype view
Huang, W.; Ye, M.; Shi, Z.; Li, H.; and Du, B. 2023 · 2023
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Power consumption forecast model using ensemble learning for smart grid
Kumar, J.; Gupta, R.; Saxena, D.; and Singh, A. K. 2023 · 2023
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Revisiting weighted aggregation in federated learning with neural networks
Li, Z.; Lin, T.; Shang, X.; and Wu, C. 2023 · 2023
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DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization
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Sunrise: A simple unified framework for ensemble learning in deep reinforcement learning
Lee, K.; Laskin, M.; Srinivas, A.; and Abbeel, P. 2021 · 2021
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Federated multi-task learning under a mixture of distributions
Marfoq, O.; Neglia, G.; Bellet, A.; Kameni, L.; and Vidal, R. 2021 · 2021
Cited alongside, same era.
Multi-task federated learning for personalised deep neural networks in edge computing
Mills, J.; Hu, J.; and Min, G. 2021 · 2021
Cited alongside, same era.
Recent advances on neural network pruning at initialization
Wang, H.; Qin, C.; Bai, Y.; Zhang, Y.; and Fu, Y. 2021 · 2021
Cited alongside, same era.
Domain adaptive ensemble learning
Zhou, K.; Yang, Y.; Qiao, Y.; and Xiang, T. 2021 · 2021
Cited alongside, same era.
Hyperstyle: Stylegan inversion with hypernetworks for real image editing
Alaluf, Y.; Tov, O.; Mokady, R.; Gal, R.; and Bermano, A. 2022 · 2022
Cited alongside, same era.
Lv, Z.; Zhang, W.; Zhang, S.; Kuang, K.; Wang, F.; Wang, Y.; Chen, Z.; Shen, T.; Yang, H.; Ooi, B. C.; and Wu, F. 2023 · 2023
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Llm-pruner: On the structural pruning of large language models
Ma, X.; Fang, G.; and Wang, X. 2023 · 2023
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Task-specific skill localization in fine-tuned language models
Panigrahi, A.; Saunshi, N.; Zhao, H.; and Arora, S. 2023 · 2023
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Personalized federated learning under mixture of distributions
Wu, Y.; Zhang, S.; Yu, W.; Liu, Y.; Gu, Q.; Zhou, D.; Chen, H.; and Cheng, W. 2023 · 2023
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Feddisco: Federated learning with discrepancy-aware collaboration
Ye, R.; Xu, M.; Wang, J.; Xu, C.; Chen, S.; and Wang, Y. 2023 · 2023
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A dynamic clustering ensemble learning approach for crude oil price forecasting
Yuan, J.; Li, J.; and Hao, J. 2023 · 2023
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A three-regime model of network pruning
Zhou, Y.; Yang, Y.; Chang, A.; and Mahoney, M. W. 2023 · 2023
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Structural pruning for diffusion models
Fang, G.; Ma, X.; and Wang, X. 2024 · 2024
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BERT-based ensemble learning for multi-aspect hate speech detection
Mazari, A. C.; Boudoukhani, N.; and Djeffal, A. 2024 · 2024
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Bridging Local Details and Global Context in Text-Attributed Graphs
Wang, Y.; Zhu, Y.; Zhang, W.; Zhuang, Y.; Liyunfei, L.; and Tang, S. 2024 · 2024
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HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models
Zhang, W.; Lin, T.; Liu, J.; Shu, F.; Li, H.; Zhang, L.; Wanggui, H.; Zhou, H.; Lv, Z.; Jiang, H.; et al. 2024 · 2024
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Efficient Tuning and Inference for Large Language Models on Textual Graphs
Zhu, Y.; Wang, Y.; Shi, H.; and Tang, S. 2024 · 2024
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