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Tensor networks are efficient for extremely high-dimensional representation, but their model selection, known as tensor network structure search (TN-SS), is a challenging problem.
Efficient SVM regression training with SMO
Flake, G. W. and Lawrence, S · 2002
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Simulating quantum computation by contracting tensor networks
Markov, I. L. and Shi, Y · 2008
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Contour detection and hierarchical image segmentation
Arbelaez, P., Maire, M., Fowlkes, C., and Malik, J · 2010
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Tensor decompositions for learning latent variable models
Anandkumar, A., Ge, R., Hsu, D., Kakade, S. M., and Telgarsky, M · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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A practical introduction to tensor networks: Matrix product states and projected entangled pair states
Orús, R · 2014
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Scalable bayesian low-rank decomposition of incomplete multiway tensors
Rai, P., Wang, Y., Guo, S., Chen, G., Dunson, D., and Carin, L · 2014
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Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods
Tüfekci, P · 2014
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Bayesian CP factorization of incomplete tensors with automatic rank determination
Zhao, Q., Zhang, L., and Cichocki, A · 2015
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Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions
Cichocki, A., Lee, N., Oseledets, I., Phan, A.-H., Zhao, Q., Mandic, D. P., et al · 2016
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Quality diversity: A new frontier for evolutionary computation
Pugh, J. K., Soros, L. B., and Stanley, K. O · 2016
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Supervised learning with tensor networks
Stoudenmire, E. and Schwab, D. J · 2016
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Smooth PARAFAC decomposition for tensor completion
Yokota, T., Zhao, Q., and Cichocki, A · 2016
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Tensor networks for dimensionality reduction and large-scale optimization: Part 2 applications and future perspectives
Cichocki, A., Phan, A.-H., Zhao, Q., Lee, N., Oseledets, I., Sugiyama, M., Mandic, D. P., et al · 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
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Scalable Gaussian processes with billions of inducing inputs via tensor train decomposition
Izmailov, P., Novikov, A., and Kropotov, D · 2018
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Expressive power of tensor-network factorizations for probabilistic modeling
Glasser, I., Sweke, R., Pancotti, N., Eisert, J., and Cirac, I · 2019
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Exploring unexplored tensor network decompositions for convolutional neural networks
Hayashi, K., Yamaguchi, T., Sugawara, Y., and Maeda, S.-i · 2019
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Differentiable programming tensor networks
Liao, H.-J., Liu, J.-G., Wang, L., and Xiang, T · 2019
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Tensor networks for complex quantum systems
Orús, R · 2019
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A novel rank selection scheme in tensor ring decomposition based on reinforcement learning for deep neural networks
Cheng, Z., Li, B., Fan, Y., and Bao, Y · 2020
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Adaptive learning of tensor network structures
Hashemizadeh, M., Liu, M., Miller, J., and Rabusseau, G · 2020
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Tensor regression networks
Kossaifi, J., Lipton, Z. C., Kolbeinsson, A., Khanna, A., Furlanello, T., and Anandkumar, A · 2020
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Evolutionary topology search for tensor network decomposition
Li, C. and Sun, Z · 2020
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On algorithms for and computing with the tensor ring decomposition
Mickelin, O. and Karaman, S · 2020
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A blind block term decomposition of high order tensors
Cai, Y. and Li, P · 2021
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Active learning of tree tensor networks using optimal least-squares
Batude: Budget-aware neural network compression based on Tucker decomposition
Yin, M., Phan, H., Zang, X., Liao, S., and Yuan, B · 2022
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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
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Chip-chat: Challenges and opportunities in conversational hardware design
Blocklove, J., Garg, S., Karri, R., and Pearce, H · 2023
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Autonomous chemical research with large language models
Boiko, D. A., MacKnight, R., Kline, B., and Gomes, G · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al · 2023
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Haberstich, C., Nouy, A., and Perrin, G · 2021
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Bayesian tensorized neural networks with automatic rank selection
Hawkins, C. and Zhang, Z · 2021
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Heuristic rank selection with progressively searching tensor ring network
Li, N., Pan, Y., Chen, Y., Ding, Z., Zhao, D., and Xu, Z · 2021
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Bayesian low rank tensor ring for image recovery
Long, Z., Zhu, C., Liu, J., and Liu, Y · 2021
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Tensor networks for probabilistic sequence modeling
Miller, J., Rabusseau, G., and Terilla, J · 2021
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Adaptive tensor networks decomposition
Nie, C., Wang, H., and Tian, L · 2021
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Pretrained language models are symbolic mathematics solvers too!
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Evoprompting: Language models for code-level neural architecture search
Chen, A., Dohan, D. M., and So, D. R · 2023
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Geometry of tree-based tensor formats in tensor banach spaces
Falcó, A., Hackbusch, W., and Nouy, A · 2023
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Approximately optimal core shapes for tensor decompositions
Ghadiri, M., Fahrbach, M., Fu, G., and Mirrokni, V · 2023
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Automatic structural optimization of tree tensor networks
Hikihara, T., Ueda, H., Okunishi, K., Harada, K., and Nishino, T · 2023
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Mars: Masked automatic ranks selection in tensor decompositions
Kodryan, M., Kropotov, D., and Vetrov, D · 2023
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Evolution through large models
Lehman, J., Gordon, J., Jain, S., Ndousse, K., Yeh, C., and Stanley, K. O · 2023
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Alternating local enumeration (tnale): Solving tensor network structure search with fewer evaluations
Li, C., Zeng, J., Li, C., Caiafa, C. F., and Zhao, Q · 2023
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Language model crossover: Variation through few-shot prompting
Meyerson, E., Nelson, M. J., Bradley, H., Moradi, A., Hoover, A. K., and Lehman, J · 2023
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Llmatic: Neural architecture search via large language models and quality-diversity optimization
Nasir, M. U., Earle, S., Togelius, J., James, S., and Cleghorn, C · 2023
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Capabilities of gpt-4 on medical challenge problems
Nori, H., King, N., McKinney, S. M., Carignan, D., and Horvitz, E · 2023
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Mathematical discoveries from program search with large language models
Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J., Ellenberg, J. S., Wang, P., Fawzi, O., et al · 2023
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Is chatgpt the ultimate programming assistant–how far is it?
Tian, H., Lu, W., Li, T. O., Tang, X., Cheung, S.-C., Klein, J., and Bissyandé, T. F · 2023
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Large language models as optimizers
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X · 2023
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Large language models can learn rules
Zhu, Z., Xue, Y., Chen, X., Zhou, D., Tang, J., Schuurmans, D., and Dai, H · 2023
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Evolving code with a large language model
Hemberg, E., Moskal, S., and O’Reilly, U.-M · 2024
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