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Evaluating quantum generative models via imbalanced data classification ...
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Evaluating quantum generative models via imbalanced data classification ...
Evaluating quantum generative models via imbalanced data classification ...
Evaluating quantum generative models via imbalanced data classification ...
Evaluating quantum generative models via imbalanced data classification ...
Evaluating quantum generative models via imbalanced data classification ...
Evaluating quantum generative models via imbalanced data classification ...
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Figure 12 from Imbalanced Data Classification via Generative ...
The paradigm of quantum generative learning models. (a) The data ...
Figure 10 from Imbalanced Data Classification via Generative ...
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How to deal with imbalanced data with generative models - Part 1
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Illustration of the proposed imbalanced sample generation framework ...
Binary imbalanced data classification based on diversity oversampling ...
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Taxonomy of generative models. | Download Scientific Diagram
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Model accuracy for the imbalanced dataset | Download Scientific Diagram
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Guide to Classification on Imbalanced Datasets
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Figure 2 from Cluster-based Generative Adversarial Network Imbalanced ...
Addressing the class imbalance in tabular datasets from a generative ...
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Hybrid scheme for training a quantum generative model | Download ...
The flowchart of training the CNN-LSTM model with imbalanced data ...
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imbalanced-data · GitHub Topics · GitHub
Model accuracy for the imbalanced dataset | Download Scientific Diagram
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%20as%20their%20simple%20and%20elegant%20quantum%20counterpart.%20Through%20a%20non-unitary%20forward%20process%2C%20any%20target%20quantum%20state%20can%20be%20transformed%20into%20a%20completely%20mixed%20state%20that%20has%20the%20highest%20entropy%20and%20maximum%20uncertainty%20about%20the%20system.%20A%20trainable%20backward%20process%20is%20used%20to%20recover%20the%20former%20from%20the%20latter.%20The%20design%20requirements%20for%20its%20backward%20process%20includes%20non-unitarity%20and%20small%20parameter%20count.%20We%20introduce%20partial%20trace%20operations%20to%20enforce%20non-unitary%20and%20reduce%20the%20number%20of%20trainable%20parameters%20by%20using%20a%20parameter-sharing%20strategy%20and%20incorporating%20temporal%20information%20as%20an%20input%20in%20the%20backward%20process.%20We%20present%20QGDM's%20resource-efficient%20version%20to%20reduce%20auxiliary%20qubits%20while%20preserving%20generative%20capabilities.%20QGDM%20exhibits%20faster%20convergence%20than%20Quantum%20Generative%20Adversarial%20Network%20(QGAN)%20because%20its%20adopted%20convex-based%20optimization%20can%20result%20in%20faster%20convergence.%20The%20results%20of%20comparing%20it%20with%20QGAN%20demonstrate%20its%20effectiveness%20in%20generating%20both%20pure%20and%20mixed%20quantum%20states.%20It%20can%20achieve%2053.02%25%20higher%20fidelity%20in%20mixed-state%20generation%20than%20QGAN.%20The%20results%20highlight%20its%20great%20potential%20to%20tackle%20challenging%20quantum%20generation%20tasks.)
