rmdubash@sun2.cs.uh.edu (08/06/88)
I am currently working on stochastic relaxation and relaxation algorithms for finely grained parallel architectures. In particular, I am studying their implementation on neural and connectionist models, with emphasis on inherent fault tolerance property of such implementations. I will be grateful if any of you can provide me with pointers, references etc. on this ( or related ) topics. Thanks. _______________________________________________________________________________ Rumi Dubash, Computer Science, Univ. of Houston, Internet : rmdubash@sun2.cs.uh.edu U.S.Mail : R.M.Dubash, Computer Science Dept., Univ. of Houston,
manj@brand.usc.edu (B. S. Manjunath) (08/07/88)
In article <824@uhnix1.uh.edu> rmdubash@sun2.cs.uh.edu () writes: >I am currently working on stochastic relaxation and relaxation algorithms for >finely grained parallel architectures. In particular, I am studying their >implementation on neural and connectionist models, with emphasis on inherent >fault tolerance property of such implementations. > >I will be grateful if any of you can provide me with pointers, references etc. >on this ( or related ) topics. >Rumi Dubash, Computer Science, Univ. of Houston, Geman and Geman (1984) is an excellent paper to start with. It also contains lot of refernces. The paper mainly deals with Markov Random Fields and applications to image processing. S.Geman and D.Geman,"Stochastic relaxation, Gibbs distributions and the bayesian restoration of images", IEEE trans. on pattern analysis and machine intelligence", PAMI-6,Nov 1984, pp. 721-742. Another reference that I feel might be useful is Marroquin,J.L. Ph. D Thesis "Probabilistic solution of Inverse problems", M.I.T. 1985. bs manjunath.