Variational Learning in Graphical Models and Neural Networks

1998 ◽  
pp. 13-22 ◽  
Author(s):  
Christopher M. Bishop
Keyword(s):  
Neural Networks ◽  
Graphical Models ◽  
Variational Learning

scholarly journals Scene Labeling using H-LSTM by Predicting the Pixels using Various Functions

2019 ◽  
Vol 8 (3) ◽  
pp. 1179-1185
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Graphical Models ◽  
Color Image ◽  
Computation Time ◽  
Reference Pattern ◽  
The Neural Network ◽  
Scene Labeling ◽  
Color Transformation ◽  
Color Enhancement

Scene Labeling plays an important role in Scene understanding in which the pixels are classified and grouped together to form a label of an image. For this concept, so many neural networks are applied and they produce fine results. Without any preprocessing methods, the system works very well compared to methods which are using preprocessing and some graphical models. Here the neural network used to extract the features is Hierarchical LSTM method, which already gives greater result in Scene parsing in the existing method. In order to reduce the computation time and increase the Pixel accuracy HLSTM is used with Makecform and Softmax functions were applied. The color transformation is applied using the Makecform function. The color enhancement of images has given object as input to H-LSTM function to identify the objects based on the referential shape and color. H-LSTM constructs the neural network by taking the reference pattern and the corresponding label as input. The pixels present in the neighbourhood identified with the help of neural network. In this method, the color image is converted into greyscale and then the Hierarchical LSTM method is applied. Therefore, this method gives greater results when it is implemented in Matlab tool, based on pixel accuracy and computation time when compared to other methods.

scholarly journals Variational learning for quantum artificial neural networks

2020 ◽  
Author(s):  
Francesco Tacchino ◽  
Panagiotis Kl. Barkoutsos ◽  
Chiara Macchiavello ◽  
Dario Gerace ◽  
Ivano Tavernelli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
Variational Learning

scholarly journals Variational learning for quantum artificial neural networks

2021 ◽  
pp. 1-1
Author(s):  
Francesco Tacchino ◽  
Stefano Mangini ◽  
Panagiotis Kl. Barkoutsos ◽  
Chiara Macchiavello ◽  
Dario Gerace ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
Variational Learning

scholarly journals On Similarities between Inference in Game Theory and Machine Learning

2008 ◽  
Vol 33 ◽  
pp. 259-283 ◽  
Author(s):  
I. Rezek ◽  
D. S. Leslie ◽  
S. Reece ◽  
S. J. Roberts ◽  
A. Rogers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Game Theory ◽  
Graphical Models ◽  
Learning Algorithm ◽  
Mean Field ◽  
Mixture Distribution ◽  
Fictitious Play ◽  
Strongly Connected ◽  
Recent Developments ◽  
Variational Learning

In this paper, we elucidate the equivalence between inference in game theory and machine learning. Our aim in so doing is to establish an equivalent vocabulary between the two domains so as to facilitate developments at the intersection of both fields, and as proof of the usefulness of this approach, we use recent developments in each field to make useful improvements to the other. More specifically, we consider the analogies between smooth best responses in fictitious play and Bayesian inference methods. Initially, we use these insights to develop and demonstrate an improved algorithm for learning in games based on probabilistic moderation. That is, by integrating over the distribution of opponent strategies (a Bayesian approach within machine learning) rather than taking a simple empirical average (the approach used in standard fictitious play) we derive a novel moderated fictitious play algorithm and show that it is more likely than standard fictitious play to converge to a payoff-dominant but risk-dominated Nash equilibrium in a simple coordination game. Furthermore we consider the converse case, and show how insights from game theory can be used to derive two improved mean field variational learning algorithms. We first show that the standard update rule of mean field variational learning is analogous to a Cournot adjustment within game theory. By analogy with fictitious play, we then suggest an improved update rule, and show that this results in fictitious variational play, an improved mean field variational learning algorithm that exhibits better convergence in highly or strongly connected graphical models. Second, we use a recent advance in fictitious play, namely dynamic fictitious play, to derive a derivative action variational learning algorithm, that exhibits superior convergence properties on a canonical machine learning problem (clustering a mixture distribution).

scholarly journals Deep Bucket Elimination

2021 ◽  
Author(s):  
Yasaman Razeghi ◽  
Kalev Kask ◽  
Yadong Lu ◽  
Pierre Baldi ◽  
Sakshi Agarwal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Partition Function ◽  
Graphical Models ◽  
State Of The Art ◽  
Proof Of Concept ◽  
Accurate Approximation ◽  
Current State

Bucket Elimination (BE) is a universal inference scheme that can solve most tasks over probabilistic and deterministic graphical models exactly. However, it often requires exponentially high levels of memory (in the induced-width) preventing its execution. In the spirit of exploiting Deep Learning for inference tasks, in this paper, we will use neural networks to approximate BE. The resulting Deep Bucket Elimination (DBE) algorithm is developed for computing the partition function. We provide a proof-of-concept empirically using instances from several different benchmarks, showing that DBE can be a more accurate approximation than current state-of-the-art approaches for approximating BE (e.g. the mini-bucket schemes), especially when problems are sufficiently hard.

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