# persistent contrastive divergence

## persistent contrastive divergence

In the next post, I will show you an alternative algorithm that has gained a lot of popularity called persistent contrastive divergence (PCD), before we finally set out to implement an restricted Boltzmann machine on a GPU using the TensorFlow framework. In self-supervised learning, we use one part of the input to predict the other parts. Parameters n_components int, default=256. This will create flat spots in the energy function and affect the overall performance. $$\gdef \E {\mathbb{E}}$$ share | improve this answer | follow | edited Jan 25 '19 at 1:40. It instead defines different heads $f$ and $g$, which can be thought of as independent layers on top of the base convolutional feature extractor. Empiri- cal results on various undirected models demon-strate that the particle ﬁltering technique we pro-pose in this paper can signiﬁcantly outperform MCMC-MLE. Recently, Tieleman [8] proposed a faster alternative to CD, called Persistent Contrastive Divergence (PCD), which employs a persistent Markov chain to approximate hi. $$\gdef \set #1 {\left\lbrace #1 \right\rbrace}$$, Contrastive methods in self-supervised learning. :˫*�FKarV�XD;/s+�$E~ �(!�q�؇��а�eEE�ϫ � �in�Q ��u ��ˠ � ��ÿ' However, the system does not scale well as the dimensionality increases. So there is no guarantee that we can shape the energy function by simply pushing up on lots of different locations. learning_rate float, default=0.1. One of which is methods that are similar to Maximum Likelihood method, which push down the energy of data points and push up everywhere else. We will briefly discuss the basic idea of contrastive divergence. Contrastive Divergence or Persistent Contrastive Divergence are often used for training the weights of Restricted Boltzmann machines. The final loss function, therefore, allows us to build a model that pushes the energy down on similar pairs while pushing it up on dissimilar pairs. As a result, we choose a similarity metric (such as cosine similarity) and a loss function that maximizes the similarity between$h$and$h’$. The most commonly used learning algorithm for restricted Boltzmann machines is contrastive divergence which starts a Markov chain at a data point and runs the chain for only a few iterations to get a cheap, low variance estimate of the sufficient statistics under the model. �J�[�������f�. We show how these ap-proaches are related to each other and discuss the relative merits of each approach. Researchers have found empirically that applying contrastive embedding methods to self-supervised learning models can indeed have good performances which rival that of supervised models. $$\gdef \D {\,\mathrm{d}}$$ Using Fast Weights to Improve Persistent Contrastive Divergence where P is the distribution of the training data and Qθ is the model’s distribution. $$\gdef \matr #1 {\boldsymbol{#1}}$$ This corresponds to standard CD without reinitializing the visible units of the Markov chain with a training sample each time we want to draw a sample . Instead of running a (very) short Gibbs sampler once for every iteration, the algorithm uses the final state of the previous Gibbs sampler as the initial start for the next iteration. Keep doing so will eventually lower the energy of$y$. Hinton, Geoffrey E. 2002. Persistent Contrastive Divergence addresses this. We can then update the parameter of our energy function by comparing$y$and the contrasted sample$\bar y$with some loss function. So we also generate negative samples ($x_{\text{neg}}$,$y_{\text{neg}}$), images with different content (different class labels, for example). One of the refinements of contrastive divergence is persistent contrastive divergence. Parameters are estimated using Stochastic Maximum Likelihood (SML), also known as Persistent Contrastive Divergence (PCD) [2]. Contrastive divergence is an approximate ML learning algorithm pro-posed by Hinton (2001). We call this a positive pair. Viewed 3k times 9. Ask Question Asked 6 years, 7 months ago. This is the case of Restricted Boltzmann Machines (RBM) and its learning algorithm Contrastive Divergence (CD). proposed in RBM. The most commonly used learning algorithm for restricted Boltzmann machines is contrastive divergence which starts a Markov chain at a data point and runs the chain for only a few iterations to get a cheap, low variance estimate of the sufficient statistics under the model. called Persistent Contrastive Divergence (PCD) solves the sampling with a related method, only that the negative par- ticle is not sampled from the positive particle, but rather There are other contrastive methods such as contrastive divergence, Ratio Matching, Noise Contrastive Estimation, and Minimum Probability Flow. 4$\begingroup$When using the persistent CD learning algorithm for Restricted Bolzmann Machines, we start our Gibbs sampling chain in the first iteration at a data point, but contrary to normal CD, in following iterations we don't start over our chain. 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