A Learning Algorithm for Boltzmann Machines*

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Learning Algorithm for Boltzmann Machines

The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a problem in o very short time. One kind of computation for which massively porollel networks appear to ...

متن کامل

A New Learning Algorithm for Mean Field Boltzmann Machines

We present a new learning algorithm for Mean Field Boltzmann Machines based on the contrastive divergence optimization criterion. In addition to minimizing the divergence between the data distribution and the equilibrium distribution that the network believes in, we maximize the divergence between one-step reconstructions of the data and the equilibrium distribution. This eliminates the need to...

متن کامل

Convolutional Restricted Boltzmann Machines for Feature Learning

In this thesis, we present a method for learning problem-specific hierarchical features specialized for vision applications. Recently, a greedy layerwise learning mechanism has been proposed for tuning parameters of fully connected hierarchical networks. This approach views layers of a network as Restricted Boltzmann Machines (RBM), and trains them separately from the bottom layer upwards. We d...

متن کامل

Improved Learning Algorithms for Restricted Boltzmann Machines

A restricted Boltzmann machine (RBM) is often used as a building block for constructing deep neural networks and deep generative models which have gained popularity recently as one way to learn complex and large probabilistic models. In these deep models, it is generally known that the layer-wise pretraining of RBMs facilitates finding a more accurate model for the data. It is, hence, important...

متن کامل

Learning and Evaluating Boltzmann Machines

We provide a brief overview of the variational framework for obtaining deterministic approximations or upper bounds for the log-partition function. We also review some of the Monte Carlo based methods for estimating partition functions of arbitrary Markov Random Fields. We then develop an annealed importance sampling (AIS) procedure for estimating partition functions of restricted Boltzmann mac...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Cognitive Science

سال: 1985

ISSN: 0364-0213

DOI: 10.1207/s15516709cog0901_7