Quantum semi-supervised generative adversarial network for enhanced data classification
暂无分享,去创建一个
[1] Ji-Rong Wen,et al. PSGAN: A Minimax Game for Personalized Search with Limited and Noisy Click Data , 2019, SIGIR.
[2] Abhinav Anand,et al. Experimental demonstration of a quantum generative adversarial network for continuous distributions , 2020, ArXiv.
[3] Daniel Herr,et al. Anomaly detection with variational quantum generative adversarial networks , 2020, Quantum Science and Technology.
[4] Kristan Temme,et al. Supervised learning with quantum-enhanced feature spaces , 2018, Nature.
[5] Seth Lloyd,et al. Quantum Generative Adversarial Learning. , 2018, Physical review letters.
[6] Stefan Woerner,et al. Quantum Generative Adversarial Networks for learning and loading random distributions , 2019, npj Quantum Information.
[7] Peng Zhang,et al. IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models , 2017, SIGIR.
[8] Alexei A. Efros,et al. Generative Visual Manipulation on the Natural Image Manifold , 2016, ECCV.
[9] Fan Yang,et al. Good Semi-supervised Learning That Requires a Bad GAN , 2017, NIPS.
[10] Ashley Montanaro,et al. Achieving quantum supremacy with sparse and noisy commuting quantum computations , 2016, 1610.01808.
[11] Simone Severini,et al. Quantum machine learning: a classical perspective , 2017, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[12] Seth Lloyd,et al. Quantum random access memory. , 2007, Physical review letters.
[13] Jiahai Wang,et al. CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generation , 2019, AAAI.
[14] Lei Wang,et al. Differentiable Learning of Quantum Circuit Born Machine , 2018, Physical Review A.
[15] Shenggen Zheng,et al. Quantum generative adversarial network for generating discrete data , 2018 .
[16] Dacheng Tao,et al. Experimental Quantum Generative Adversarial Networks for Image Generation , 2020, Physical Review Applied.
[17] Francesco Petruccione,et al. Quantum classifier with tailored quantum kernel , 2019 .
[18] Ievgeniia Oshurko. Quantum Machine Learning , 2020, Quantum Computing.
[19] Shin Ishii,et al. Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] Yongqiang Zhang,et al. SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network , 2018, ECCV.
[21] Holger H. Hoos,et al. A survey on semi-supervised learning , 2019, Machine Learning.
[22] Franco Nori,et al. Quantum State Tomography with Conditional Generative Adversarial Networks , 2020, Physical review letters.
[23] R. Jozsa,et al. Classical simulation of commuting quantum computations implies collapse of the polynomial hierarchy , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[24] Xiaoming Liu,et al. Representation Learning by Rotating Your Faces , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] Simone Severini,et al. Adversarial quantum circuit learning for pure state approximation , 2018, New Journal of Physics.
[26] David M. Reif,et al. Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology , 2021, PLoS Comput. Biol..
[27] Junmei Wang,et al. Deep convolutional generative adversarial network (dcGAN) models for screening and design of small molecules targeting cannabinoid receptors. , 2019, Molecular pharmaceutics.
[28] M. Schuld,et al. Prediction by linear regression on a quantum computer , 2016, 1601.07823.
[29] Yu Qiao,et al. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks , 2018, ECCV Workshops.
[30] Augustus Odena,et al. Semi-Supervised Learning with Generative Adversarial Networks , 2016, ArXiv.
[31] A. Harrow,et al. Quantum Supremacy through the Quantum Approximate Optimization Algorithm , 2016, 1602.07674.
[32] Philippe Thomas. Review of Semi-supervised learning by O. Chapelle, B. Schölkopf, and A. Zien, Eds. London, UK, MIT Press, 2006 , 2009 .
[33] Shu-Hao Wu,et al. Quantum generative adversarial learning in a superconducting quantum circuit , 2018, Science Advances.
[34] Byron Boots,et al. Learning and Inference in Hilbert Space with Quantum Graphical Models , 2018, NeurIPS.
[35] Zhenan Sun,et al. A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications , 2020, IEEE Transactions on Knowledge and Data Engineering.
[36] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[37] Ashley Montanaro,et al. Average-case complexity versus approximate simulation of commuting quantum computations , 2015, Physical review letters.
[38] Ruili Wang,et al. Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies , 2020, Inf. Fusion.
[39] Sergey Nikolenko,et al. druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico. , 2017, Molecular pharmaceutics.
[40] Shouvanik Chakrabarti,et al. Quantum Wasserstein Generative Adversarial Networks , 2019, NeurIPS.
[41] Travis S. Humble,et al. Quantum supremacy using a programmable superconducting processor , 2019, Nature.
[42] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[43] Matteo A. C. Rossi,et al. IBM Q Experience as a versatile experimental testbed for simulating open quantum systems , 2019, npj Quantum Information.
[44] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[45] Alan Aspuru-Guzik,et al. Variational Quantum Generators: Generative Adversarial Quantum Machine Learning for Continuous Distributions , 2019, Advanced Quantum Technologies.
[46] Sebastian Thrun,et al. Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.
[47] Nathan Killoran,et al. Quantum generative adversarial networks , 2018, Physical Review A.
[48] Chuan Li,et al. Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks , 2016, ECCV.
[49] Jiangping Hu,et al. Learning and Inference on Generative Adversarial Quantum Circuits , 2018, Physical Review A.
[50] Masanao Ozawa,et al. Soundness and completeness of quantum root-mean-square errors , 2018, npj Quantum Information.
[51] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[52] M. Schuld,et al. Circuit-centric quantum classifiers , 2018, Physical Review A.
[53] Jacob biamonte,et al. Quantum machine learning , 2016, Nature.
[54] Kai Xu,et al. Quantum generative adversarial networks with multiple superconducting qubits , 2020, npj Quantum Information.
[55] Eugene Lin,et al. Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design , 2020, Molecules.