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Found 5 papers, 1 papers with code
Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
Recent theoretical results in quantum machine learning have demonstrated a general trade-off between the expressive power of quantum neural networks (QNNs) and their trainability; as a corollary of these results, practical exponential separations in expressive power over classical machine learning models are believed to be infeasible as such QNNs take a time to train that is exponential in the model size.
Many-body localized hidden generative models
Born machines are quantum-inspired generative models that leverage the probabilistic nature of quantum states.
Pairwise Emotional Relationship Recognition in Drama Videos: Dataset and Benchmark
We expect the ERATO as well as our proposed SMTA to open up a new way for PERR task in video understanding and further improve the research of multi-modal fusion methodology.
Enhancing Generative Models via Quantum Correlations
Generative modeling using samples drawn from the probability distribution constitutes a powerful approach for unsupervised machine learning.
An efficient quantum algorithm for generative machine learning
A central task in the field of quantum computing is to find applications where quantum computer could provide exponential speedup over any classical computer.
