Corpus ID: 53111154

Learning and Inference in Hilbert Space with Quantum Graphical Models

@inproceedings{Srinivasan2018LearningAI,
  title={Learning and Inference in Hilbert Space with Quantum Graphical Models},
  author={Siddarth Srinivasan and C. Downey and Byron Boots},
  booktitle={NeurIPS},
  year={2018}
}
Quantum Graphical Models (QGMs) generalize classical graphical models by adopting the formalism for reasoning about uncertainty from quantum mechanics. [...] Key Result We present experimental results showing that HSE-HQMMs are competitive with state-of-the-art models like LSTMs and PSRNNs on several datasets, while also providing a nonparametric method for maintaining a probability distribution over continuous-valued features.Expand
11 Citations
Learning Quantum Graphical Models using Constrained Gradient Descent on the Stiefel Manifold
  • 1
  • PDF
Expressiveness and Learning of Hidden Quantum Markov Models
  • 1
  • PDF
Kernel Mean Embeddings of Von Neumann-Algebra-Valued Measures
  • PDF
Efficient Discrete Feature Encoding for Variational Quantum Classifier
  • 2
  • PDF
...
1
2
...

References

SHOWING 1-10 OF 20 REFERENCES
Learning Hidden Quantum Markov Models
  • 15
  • PDF
Quantum Machine Learning in Feature Hilbert Spaces.
  • 207
  • PDF
Quantum Graphical Models and Belief Propagation
  • 104
  • PDF
A Probabilistic Graphical Model of Quantum Systems
  • C. Yeang
  • Computer Science
  • 2010 Ninth International Conference on Machine Learning and Applications
  • 2010
  • 5
  • PDF
Hidden Quantum Markov Models and non-adaptive read-out of many-body states
  • 37
  • PDF
Hilbert Space Embeddings of Predictive State Representations
  • 77
  • PDF
Hilbert Space Embeddings of PSRs
  • 2
  • PDF
Hilbert space embeddings of conditional distributions with applications to dynamical systems
  • 224
  • PDF
Predictive State Recurrent Neural Networks
  • 39
  • PDF
Hilbert Space Embeddings of Hidden Markov Models
  • 205
  • PDF
...
1
2
...