Continuous-Valued Probabilistic Behavior in A VLSI Generative Model  

Oleh:

Chen, Hsin ; Fleury, P. C. D. ; Murray, A. F.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 17 no. 3 (May 2006), page 755-770.
Topik: probabilistic thinking; continuous - valued; probabilistic behaviour; VLSI generative model

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This paper presents the VLSI implementation of the continuous restricted Boltzmann machine (CRBM), a probabilistic generative model that is able to model continuous - valued data with a simple and hardware - amenable training algorithm. The full CRBM system consists of stochastic neurons whose continuous - valued probabilistic behaviour is mediated by injected noise. Integrating on - chip training circuits, the full CRBM system provides a platform for exploring computation with continuous - valued probabilistic behavior in VLSI. The VLSI CRBM’s ability both to model and to regenerate continuous - valued data distributions is examined and limitations on its performance are highlighted and discussed.

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