Equivalence of restricted Boltzmann machines and tensor network states
The restricted Boltzmann machine (RBM) is one of the fundamental building blocks of deep
learning. RBM finds wide applications in dimensional reduction, feature extraction, and …
learning. RBM finds wide applications in dimensional reduction, feature extraction, and …
On the number of response regions of deep feed forward networks with piece-wise linear activations
This paper explores the complexity of deep feedforward networks with linear pre-synaptic
couplings and rectified linear activations. This is a contribution to the growing body of work …
couplings and rectified linear activations. This is a contribution to the growing body of work …
[图书][B] Machine learning with neural networks: an introduction for scientists and engineers
B Mehlig - 2021 - books.google.com
This modern and self-contained book offers a clear and accessible introduction to the
important topic of machine learning with neural networks. In addition to describing the …
important topic of machine learning with neural networks. In addition to describing the …
Information perspective to probabilistic modeling: Boltzmann machines versus born machines
We compare and contrast the statistical physics and quantum physics inspired approaches
for unsupervised generative modeling of classical data. The two approaches represent …
for unsupervised generative modeling of classical data. The two approaches represent …
Quantum enhancements for deep reinforcement learning in large spaces
S Jerbi, LM Trenkwalder, H Poulsen Nautrup… - PRX Quantum, 2021 - APS
Quantum algorithms have been successfully applied to provide computational speed ups to
various machine-learning tasks and methods. A notable exception to this has been deep …
various machine-learning tasks and methods. A notable exception to this has been deep …
Modeling sequences with quantum states: a look under the hood
TD Bradley, EM Stoudenmire… - … Learning: Science and …, 2020 - iopscience.iop.org
Classical probability distributions on sets of sequences can be modeled using quantum
states. Here, we do so with a quantum state that is pure and entangled. Because it is …
states. Here, we do so with a quantum state that is pure and entangled. Because it is …
Deep, skinny neural networks are not universal approximators
J Johnson - arXiv preprint arXiv:1810.00393, 2018 - arxiv.org
In order to choose a neural network architecture that will be effective for a particular
modeling problem, one must understand the limitations imposed by each of the potential …
modeling problem, one must understand the limitations imposed by each of the potential …
Restricted boltzmann machines: Introduction and review
G Montúfar - Information Geometry and Its Applications: On the …, 2018 - Springer
The restricted Boltzmann machine is a network of stochastic units with undirected
interactions between pairs of visible and hidden units. This model was popularized as a …
interactions between pairs of visible and hidden units. This model was popularized as a …
Symmetric tensor networks for generative modeling and constrained combinatorial optimization
J Lopez-Piqueres, J Chen… - … Learning: Science and …, 2023 - iopscience.iop.org
Constrained combinatorial optimization problems abound in industry, from portfolio
optimization to logistics. One of the major roadblocks in solving these problems is the …
optimization to logistics. One of the major roadblocks in solving these problems is the …