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| Graphical representation of the restricted Boltzmann machine. Visible layer (observed variables) is comprised of units í µí±£ and the hidden layer (latent variables) is comprised of units ℎ. The layers are fully connected, and the connections W are bidirectional.
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... one associates a) the average field í µí¼ B and effective action Π[í µí¼ B ] with the layer of visible spins í µí±£ of an RBM ( Fig. 1) like structure and b) í µí¼ and bare action í µí±[í µí¼] with the layer of hidden spins ℎ thereof, after suitable discretization, from (9) and (17), This model, however, suffers from a major issue computationally. In general, the effective functional Π is not solvable perturbatively in í µí¼ B owing to divergent integrals (similar ...
Context 2
... one associates a) the average field í µí¼ B and effective action Π[í µí¼ B ] with the layer of visible spins í µí±£ of an RBM ( Fig. 1) like structure and b) í µí¼ and bare action í µí±[í µí¼] with the layer of hidden spins ℎ thereof, after suitable discretization, from (9) and (17), This model, however, suffers from a major issue computationally. In general, the effective functional Π is not solvable perturbatively in í µí¼ B owing to divergent integrals (similar ...
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... Since then, the use of ML in condensed matter physics has rapidly expanded to include a variety of techniques [3][4][5]. These and their applications can be broadly grouped into two categories: supervised ML (SML), in which the input data to train the machine is labeled [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]; and unsupervised ML (UML), in which the input data is unlabeled and the machine proposes its own classification scheme [10,[19][20][21][22][23][24][25][26][27][28][29][30][31]. Across these two categories, the identification of thermodynamic phases in various models has remained a central theme. ...
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