Multi-Prediction Deep Boltzmann Machines

2013-12-01 · NeurIPS 2013 12 · Ian Goodfellow, Mehdi Mirza, Aaron Courville, Yoshua Bengio

We introduce the Multi-Prediction Deep Boltzmann Machine (MP-DBM). The MP-DBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent nets that share parameters and approximately solve different inference problems. Prior methods of training DBMs either do not perform well on classification tasks or require an initial learning pass that trains the DBM greedily, one layer at a time. The MP-DBM does not require greedy layerwise pretraining, and outperforms the standard DBM at classification, classification with missing inputs, and mean field prediction tasks.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Classification General Classification Prediction

Methods 이 논문이 사용한 방법론

Deep Boltzmann Machine 설명 없음

Max-Margin Boltzmann Machines for Object Segmentation

2014-06-01 · CVPR 2014 6 · Jimei Yang, Simon Safar, Ming-Hsuan Yang

We present Max-Margin Boltzmann Machines (MMBMs) for object segmentation. MMBMs are essentially a class of Conditional Boltzmann Machines that model the joint distribution of hidden variables and output labels conditione…

Object Semantic Segmentation

Transductive Boltzmann Machines

2018-05-21 · Mahito Sugiyama, Koji Tsuda, Hiroyuki Nakahara

We present transductive Boltzmann machines (TBMs), which firstly achieve transductive learning of the Gibbs distribution. While exact learning of the Gibbs distribution is impossible by the family of existing Boltzmann m…

Transductive Learning

Boltzmann machines as two-dimensional tensor networks

2021-05-10 · Sujie Li, Feng Pan, Pengfei Zhou, Pan Zhang

Restricted Boltzmann machines (RBM) and deep Boltzmann machines (DBM) are important models in machine learning, and recently found numerous applications in quantum many-body physics. We show that there are fundamental co…

BIG-bench Machine Learning Tensor Networks Vocal Bursts Valence Prediction

Deep Narrow Boltzmann Machines are Universal Approximators

2014-11-14 · Guido Montufar

We show that deep narrow Boltzmann machines are universal approximators of probability distributions on the activities of their visible units, provided they have sufficiently many hidden layers, each containing the same …

Restricted Boltzmann Machines for Collaborative Filtering

2007-06-07 · ICML: Proceedings of the 24th international conference on Machine learning 2007 6 · Ruslan Salakhutdinov, andriy mnih, Geoffrey Hinton

Most of the existing approaches to collaborative filtering cannot handle very large data sets. In this paper we show how a class of two-layer undirected graphical models, called Restricted Boltzmann Machines (RBM's), can…

Collaborative Filtering Recommendation Systems