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Found 8 papers, 3 papers with code
Generative Learning of Continuous Data by Tensor Networks
Beyond their origin in modeling many-body quantum systems, tensor networks have emerged as a promising class of models for solving machine learning problems, notably in unsupervised generative learning.
Balancing between the Local and Global Structures (LGS) in Graph Embedding
For a given graph, LGS aims to find a good balance between the local and global structure to preserve.
ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE
When visualizing a high-dimensional dataset, dimension reduction techniques are commonly employed which provide a single 2-dimensional view of the data.
Probabilistic Graphical Models and Tensor Networks: A Hybrid Framework
We first prove that applying decoherence to the entirety of a BM model converts it into a discrete UGM, and conversely, that any subgraph of a discrete UGM can be represented as a decohered BM.
Adaptive Epidemic Forecasting and Community Risk Evaluation of COVID-19
Pandemic control measures like lock-down, restrictions on restaurants and gatherings, social-distancing have shown to be effective in curtailing the spread of COVID-19.
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
We address this gap by showing how stationary or uniform versions of popular quantum tensor network models have equivalent representations in the stochastic processes and weighted automata literature, in the limit of infinitely long sequences.
Adaptive Learning of Tensor Network Structures
However, identifying the best tensor network structure from data for a given task is challenging.
Tensor Networks for Probabilistic Sequence Modeling
Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied within machine learning.
