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Found 7 papers, 3 papers with code
Supervised Learning with Projected Entangled Pair States
Tensor networks, a model that originated from quantum physics, has been gradually generalized as efficient models in machine learning in recent years.
Domain Adaption for Knowledge Tracing
We refer to this problem as domain adaptation for knowledge tracing (DAKT) which contains two aspects: (1) how to achieve great knowledge tracing performance in each domain.
Enhancing Item Response Theory for Cognitive Diagnosis
However, traditional IRT ignores the rich information in question texts, cannot diagnose knowledge concept proficiency, and it is inaccurate to diagnose the parameters for the questions which only appear several times.
Compressing deep neural networks by matrix product operators
A deep neural network is a parametrization of a multilayer mapping of signals in terms of many alternatively arranged linear and nonlinear transformations.
Tree Tensor Networks for Generative Modeling
Matrix product states (MPS), a tensor network designed for one-dimensional quantum systems, has been recently proposed for generative modeling of natural data (such as images) in terms of `Born machine'.
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.
Equivalence of restricted Boltzmann machines and tensor network states
Conversely, we give sufficient and necessary conditions to determine whether a TNS can be transformed into an RBM of given architectures.
