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Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network.
Predictive uncertainty quantification with compound density networks
Kristiadi, A., Däubener, S., and Fischer, A. (2019) · 1902
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A generative model for sampling high-performance and diverse weights for neural networks
Deutsch, L., Nijkamp, E., and Yang, Y. (2019) · 1905
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014) · 1958
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Learning to control fast-weight memories: An alternative to dynamic recurrent networks
Schmidhuber, J. (1992) · 1992
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A ‘self-referential’weight matrix
Schmidhuber, J. (1993) · 1993
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A hypercube-based encoding for evolving large-scale neural networks
Stanley, K. O., D’Ambrosio, D. B., and Gauci, J. (2009) · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y. (2010) · 2010
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions
Wiens, J., Guttag, J., and Horvitz, E. (2014) · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J. (2015) · 2015
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Hypernetworks
Ha, D., Dai, A. M., and Le, Q. V. (2017) · 2017
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Deep reinforcement learning: An overview
Li, Y. (2017) · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (2017) · 2017
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Hypernetworks with statistical filtering for defending adversarial examples
Sun, Z., Ozay, M., and Okatani, T. (2017) · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017) · 2017
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SMASH: One-shot model architecture search through hypernetworks
Brock, A., Lim, T., Ritchie, J., and Weston, N. (2018) · 2018
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Bayesian hypernetworks
Krueger, D., Huang, C.-W., Islam, R., Turner, R., Lacoste, A., and Courville, A. (2018) · 2018
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Stochastic hyperparameter optimization through hypernetworks
Lorraine, J. and Duvenaud, D. (2018) · 2018
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Hyperst-net: Hypernetworks for spatio-temporal forecasting
Pan, Z., Liang, Y., Zhang, J., Yi, X., Yu, Y., and Zheng, Y. (2018) · 2018
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Hypernetwork knowledge graph embeddings
Balažević, I., Allen, C., and Hospedales, T. M. (2019) · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019) · 2019
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Hypernetwork functional image representation
Klocek, S., Maziarka, Ł., Wołczyk, M., Tabor, J., Nowak, J., and Śmieja, M. (2019) · 2019
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Deep meta functionals for shape representation
Littwin, G. and Wolf, L. (2019) · 2019
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Metapruning: Meta learning for automatic neural network channel pruning
Liu, Z., Mu, H., Zhang, X., Guo, Z., Yang, X., Cheng, K.-T., and Sun, J. (2019) · 2019
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Hypergan: A generative model for diverse, performant neural networks
Ratzlaff, N. and Fuxin, L. (2019) · 2019
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Meta-learning with latent embedding optimization
Rusu, A. A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., and Hadsell, R. (2019) · 2019
Cited alongside, same era.
Graph hypernetworks for neural architecture search
Zhang, C., Ren, M., and Urtasun, R. (2019) · 2019
Cited alongside, same era.
Principled weight initialization for hypernetworks
Chang, O., Flokas, L., and Lipson, H. (2020) · 2020
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On the modularity of hypernetworks
Galanti, T. and Wolf, L. (2020) · 2020
Cited alongside, same era.
Dhp: Differentiable meta pruning via hypernetworks
Li, Y., Gu, S., Zhang, K., Van Gool, L., and Timofte, R. (2020) · 2020
Cited alongside, same era.
On infinite-width hypernetworks
Littwin, E., Galanti, T., Wolf, L., and Yang, G. (2020) · 2020
Cited alongside, same era.
Personalized federated learning using hypernetworks
Shamsian, A., Navon, A., Fetaya, E., and Chechik, G. (2021) · 2021
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Hyperspns: Compact and expressive probabilistic circuits
Shih, A., Sadigh, D., and Ermon, S. (2021) · 2021
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Hypergrid transformers: Towards a single model for multiple tasks
Tay, Y., Zhao, Z., Bahri, D., Metzler, D., and Juan, D.-C. (2021) · 2021
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Hyperstyle: Stylegan inversion with hypernetworks for real image editing
Alaluf, Y., Tov, O., Mokady, R., Gal, R., and Bermano, A. (2022) · 2022
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Hyperinverter: Improving stylegan inversion via hypernetwork
Dinh, T. M., Tran, A. T., Nguyen, R., and Hua, B.-S. (2022) · 2022
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Federated learning with heterogeneous architectures using graph hypernetworks
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Continual learning with hypernetworks
Oswald, J. V., Henning, C., Grewe, B. F., and Sacramento, J. (2020) · 2020
Cited alongside, same era.
Cream of the crop: Distilling prioritized paths for one-shot neural architecture search
Peng, H., Du, H., Yu, H., Li, Q., Liao, J., and Fu, J. (2020) · 2020
Cited alongside, same era.
Hypernetwork approach to generating point clouds
Spurek, P., Winczowski, S., Tabor, J., Zamorski, M., Zieba, M., and Trzciński, T. (2020) · 2020
Cited alongside, same era.
Meta-learning via hypernetworks
Zhao, D., Kobayashi, S., Sacramento, J., and Von Oswald, J. (2020) · 2020
Cited alongside, same era.
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · 2021
Cited alongside, same era.
Review of deep learning: Concepts, cnn architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., and Farhan, L. (2021) · 2021
Cited alongside, same era.
Litany, O., Maron, H., Acuna, D., Kautz, J., Chechik, G., and Fidler, S. (2022) · 2022
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Oh, G. and Peng, H. (2022) · 2022
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Hmoe: Hypernetwork-based mixture of experts for domain generalization
Qu, J., Faney, T., Wang, Z., Gallinari, P., Yousef, S., and de Hemptinne, J.-C. (2022) · 2022
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Hypernst: Hyper-networks for neural style transfer
Ruta, D., Gilbert, A., Motiian, S., Faieta, B., Lin, Z., and Collomosse, J. (2023) · 2022
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General hypernetwork framework for creating 3d point clouds
Spurek, P., Zieba, M., Tabor, J., and Trzcinski, T. (2022) · 2022
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Hypersound: Generating implicit neural representations of audio signals with hypernetworks
Szatkowski, F., Piczak, K. J., Spurek, P., Tabor, J., and Trzcinski, T. (2022) · 2022
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Example-based hypernetworks for out-of-distribution generalization
Volk, T., Ben-David, E., Amosy, O., Chechik, G., and Reichart, R. (2022) · 2022
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Character-level hypernetworks for hate speech detection
Wullach, T., Adler, A., and Minkov, E. (2022) · 2022
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Sylph: A hypernetwork framework for incremental few-shot object detection
Yin, L., Perez-Rua, J. M., and Liang, K. J. (2022) · 2022
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Hypermaml: Few-shot adaptation of deep models with hypernetworks
Zięba, M. (2022) · 2022
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Hypernetworks in meta-reinforcement learning
Beck, J., Jackson, M. T., Vuorio, R., and Whiteson, S. (2023) · 2023
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Quantum hypernetworks: Training binary neural networks in quantum superposition
Carrasquilla, J., Hibat-Allah, M., Inack, E., Makhzani, A., Neklyudov, K., Taylor, G. W., and Torlai, G. (2023) · 2023
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Hyperpose: Camera pose localization using attention hypernetworks
Ferens, R. and Keller, Y. (2023) · 2023
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Improving pareto front learning via multi-sample hypernetworks
Hoang, L. P., Le, D. D., Tuan, T. A., and Thang, T. N. (2023) · 2023
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Hyperposepdf - hypernetworks predicting the probability distribution on so(3)
Höfer, T., Kiefer, B., Messmer, M., and Zell, A. (2023) · 2023
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Hypernetworks for zero-shot transfer in reinforcement learning
Rezaei-Shoshtari, S., Morissette, C., Hogan, F. R., Dudek, G., and Meger, D. (2023) · 2023
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Using bottleneck adapters to identify cancer in clinical notes under low-resource constraints
Rohanian, O., Jauncey, H., Nouriborji, M., Chauhan, V. K., Gonalves, B. P., Kartsonaki, C., Clinical Characterisation Group, I., Merson, L., and Clifton, D. (2023) · 2023
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Tran, T. A., Hoang, L. P., Le, D. D., and Tran, T. N. (2023) · 2023
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Parameterized projected bellman operator
Vincent, T., Metelli, A. M., Belousov, B., Peters, J., Restelli, M., and D’Eramo, C. (2023) · 2023
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Wu, Q., Bauer, D., Chen, Y., and Ma, K.-L. (2023) · 2023
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