Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

In this regime (n > m), learning a sparse model may not lead to significant improvements inprediction accuracy(compared toadensemodel).

Weconsider the task of learning an RBM given samples generated according to it.

Forthis10] propose extensiontothe15], which roundingthevistokrandomly in Algorithm (10).

This makes parsing intractable and requires image grammars to be severely restricted in practice, for example by allowing only rectangular regions.

We will add the proof for the same in the revision. SDPs deliver mode estimates of practically the same quality across various coupling strengths.

This work addresses the problem of efficient sampling of Markov random fields (MRF). The sampling of Potts or Ising MRF is most often based on Gibbs sampling, and is thus computationally expensive. We consider in this work how to circumvent this bottleneck through a link with Gaussian Markov Random fields. The latter can be sampled in several cost-effective ways, and we introduce a mapping from real-valued GMRF to discrete-valued MRF. The resulting new class of MRF benefits from a few theoretical properties that validate the new model. Numerical results show the drastic performance gain in terms of computational efficiency, as we sample at least 35x faster than Gibbs sampling using at least 37x less energy, all the while exhibiting empirical properties close to classical MRFs.

We would like to thank the reviewers for the insightful remarks and comments. In our experiment, we show that FGNN outperforms the Max-Product algorithm. MRF whose length ranges from 15 to 45 (the potentials are generated using the same protocol as Dataset3). This also further addresses the overfitting issue raised by R1. Factor without trivial representation of fixed dimension: R3.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes projecting the parameters of an MRF onto the set of fast-mixing parameters: parameters for which MCMC quickly converges to the true distribution. The authors introduce a Euclidean projection operator that implements this property, but note that it can be difficult to apply. They then smooth it by requiring the projection to be close to an additional matrix input. This is sufficient for many cases, but can be applied repeatedly when the true Euclidean projection is required.