Boltzmann Machine & Dialog Systems


A Boltzmann machine is a type of artificial neural network that is used to model complex systems and patterns in data. It is a type of energy-based model, which means that it represents the data as a system of interconnected nodes, each of which represents a feature or variable, and the connections between nodes represent the relationships between the features. The Boltzmann machine is trained using an algorithm called contrastive divergence, which adjusts the strength of the connections between nodes in order to minimize the difference between the model’s predictions and the actual data.

Boltzmann machines can be used in a variety of applications, including image recognition, natural language processing, and dialog systems. In the context of dialog systems, a Boltzmann machine can be used to model the relationships between different words or phrases in a conversation, and to predict what a person might say next based on their previous statements. This can be useful for generating responses to user input, or for identifying patterns in the conversation that might indicate the user’s intention or goals.

To use a Boltzmann machine in a dialog system, it is first necessary to train the machine on a large dataset of conversational data, such as transcriptions of real-life conversations or dialogs generated by humans or other artificial intelligence systems. The machine can then be used to generate responses to user input by predicting the next words or phrases in the conversation based on the patterns it has learned from the training data. The quality of the responses will depend on the quality of the training data and the accuracy of the model’s predictions.


See also:

NSCA (Neural-Symbolic Cognitive Agent)

Towards deeper understanding: deep convex networks for semantic utterance classification G Tur, L Deng, D Hakkani-Tur… – Acoustics, Speech and …, 2012 – … output. In the last decade, a variety of practi- cal goal-oriented spoken dialog systems have been built for limited domains. Three … applicaitons. DBNs are stacks of Restricted Boltzmann Machines (RBMs) fol- lowed by fine tuning. RBM … Cited by 23 Related articles All 16 versions Cite Save

Recent advances in deep learning for speech research at Microsoft L Deng, J Li, JT Huang, K Yao, D Yu… – … , Speech and Signal …, 2013 – … [36] Z. Ling, L. Deng, and D. Yu. “Modeling spectral envelopes using restricted Boltzmann machines for statistical … Castro-BIeda, and R. De-Mori, “Cache neural network language models based on long distance dependencies for a spoken dialog system,” ICASSP, 2012. … Cited by 15 Related articles All 8 versions Cite Save

Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition GE Dahl, D Yu, L Deng, A Acero – Audio, Speech, and …, 2012 – … HMM baseline. The remainder of this paper is organized as follows. In section II we briefly introduce restricted Boltzmann machines (RBMs) and deep belief nets, and outline the general pre- training strategy we use. In section … Cited by 282 Related articles All 14 versions Cite Save

Phone Recognition on the TIMIT Database C Lopes, F Perdigão – Speech Technologies/Book, 2011 – … a number of applications using automatic speech recognition (ASR), including command and control, dictation, dialog systems for people with … regression, neural networks (multi-layer perceptron (MLP), time-delay neural networks (TDNN) or Boltzmann machines), support vector … Cited by 4 Related articles All 5 versions Cite Save More

The deep tensor neural network with applications to large vocabulary speech recognition D Yu, L Deng, F Seide – Audio, Speech, and Language …, 2013 – … modeling power of neural networks. In this section we briefly survey the related work. The tensor-based restricted Boltzmann machine (RBM) was proposed to model three-way interactions among pixels in [16] and to model pixel means and covariances in [17]. … Cited by 16 Related articles All 9 versions Cite Save

Attentiveness detection using continuous restricted boltzmann machine in e-learning environment J Zhou, H Luo, Q Luo, L Shen – Hybrid Learning and Education, 2009 – Springer … In: 9th International Conference on Human-Computer Interaction, New Orleans, August 2001, pp. 1538–1542 (2001) 8. Chen, H., Murray, A.: Continuous restricted boltzmann machine with an implementable training algorithm. … Affective Dialogue Systems, 36–48 (2004) 11. … Cited by 1 Related articles All 5 versions Cite Save

Challenges Of Natural Language Communication With Machines V DELIC, M SECUJSKI, N JAKOVLJEVIC – … collocutor. Key words: automatic speech recognition, text-to-speech synthesis, emotions in human-machine interaction, human-machine dialogue systems, challenges for the future … For example restricted Boltzmann machines, which … Cite Save More

Deep Architectures for Automatic Emotion Recognition Based on Lip Shape B Popovi?, S Ostrogonac, V Deli?, M Janev, I Stankovi? – … Layers are represented as the Restricted Boltzmann Machines (RBM) [3]. Deep belief networks have been successfully applied on a number of issues … Science and Technological Development, and it has been realized as a part of “Development of Dialogue Systems for Serbian … Related articles All 3 versions Cite Save More

Generating Questions from Web Community Contents. B Wang, B Liu, C Sun, X Wang, D Zhang – COLING (Demos), 2012 – … tends to be mainly applied in the interaction oriented systems (Rus et al., 2007; Harabagiu et al., 2005) (eg, computer aided education, help desk, dialog systems, etc … 3.1 The Restricted Boltzmann Machine A DBN is composed of several stacked “Restricted Boltzmann Machines … All 7 versions Cite Save More

Deep Generative and Discriminative Models for Speech Recognition L Deng – … on Speech and Audio Processing, Vol. 2, pp. 80-91, 1994. Jaitly, N. and Hinton, G. “Learning a better representation of speech sound waves using restricted Boltzmann machines,” Proc. ICASSP, 2011. Page 46. Themes: Nonlinearities Page 47. Themes: Architectures Page 48. … Related articles Cite Save More

Zero-Shot Learning and Clustering for Semantic Utterance Classification YN Dauphin, G Tur, D Hakkani-Tur, L Heck – arXiv preprint arXiv: …, 2013 – … It is satisfied by the various pretraining methods like restricted Boltzmann machines [20] and regularized auto-encoders [21, 22]. … evaluate the performance of the methods for SUC on the dataset gathered by [1]. It was compiled from utterances by users of a spoken dialog system. … Related articles All 2 versions Cite Save

Calibration of confidence measures in speech recognition D Yu, J Li, L Deng – Audio, Speech, and Language Processing, …, 2011 – … In this paper, we adopt the procedure proposed in [56][54][55] for training DBN parameters: first, train a stack of restricted Boltzmann machines (RBMs) generatively; then fine-tune all the parameters jointly using the back-propagation algorithm by maximizing … Cited by 11 Related articles All 10 versions Cite Save

Exploiting deep neural networks for detection-based speech recognition SM Siniscalchi, D Yu, L Deng, CH Lee – Neurocomputing, 2013 – Elsevier … A common practice is to initialize the parameters of each layer greedily and generatively by treating each pair of layers in DNNs as a restricted Boltzmann machine (RBM) before performing a joint optimization of all the layers [37]. … 4.1. Restricted Boltzmann machines. … Cited by 14 Related articles All 9 versions Cite Save

Application of Deep Belief Networks for Natural Language Understanding R Sarikaya, GE Hinton… – … ACM TRANSACTIONS ON …, 2014 – … 14, pp. 1771–1800, 2002. [17] GE Hinton, “A Practical Guide to Training Restricted Boltzmann Machines”, University of Toronto Machine … Ruhi Sarikaya Dr. Ruhi Sarikaya is a principal scientist and the manager of language understanding and dialog systems group at Microsoft. … Cite Save More

Machine learning methods for articulatory data JJ Berry – 2012 – … dialogue systems). One approach to dealing with context effects is to explicitly … called Contrastive Divergence (Hinton, 2002), which trains a two-layer Restricted Boltzmann Machine (RBM) (Smolensky, 1986) using Gibbs sampling (Geman and … Cited by 1 Related articles All 5 versions Cite Save

Advanced Series on Artificial Intelligence: Volume NG BOURBAKIS – World Scientific … AAAI-S6, 1986. Bobrow. DG, Kaplan, RM, Kay, M., Norman, DA, Thompson, H. and Winograd, T., “GUS, a frame-driven dialog system”, >lrli/. Intell. … Fahlman, SE ei al., “Massively parallel architectures for Al: NETL, THISTLE, and BOLTZMANN Machines”, Proc. Annual Nat. Conf. … Cite Save

Machine learning paradigms for speech recognition: An overview L Deng, X Li – 2013 – Page 1. Copyright (c) 2013 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing This article has been accepted for publication in a future issue of this journal, but has not been fully edited. … Cited by 17 Related articles All 9 versions Cite Save

Automatic Language Recognition Using Deep Neural Networks AL D?ez – 2013 – … only those in the language of interest, or for preprocessing the input speech signal in multilingual dialog systems [Ambikairajah et al … of networks was trained one layer at a time, taking advantage of unsupervised learning algorithms, which are the Restricted Boltzmann Machines. … Cited by 1 Related articles Cite Save More

Language Learning via Unsupervised Corpus Analysis B Goertzel, C Pennachin, N Geisweiller – Engineering General Intelligence …, 2014 – Springer … and affect the recognition of lower level patterns. Our approach does not use conventional deep learning archi- tectures like Deep Boltzmann machines or recurrent neural networks. Conceptually, our approach is based on a … Cite Save

An artificial neural network approach to automatic speech processing SM Siniscalchi, T Svendsen, CH Lee – Neurocomputing, 2014 – Elsevier … DNNs were implemented by treating each pair of layers in DNNs as a restricted Boltzmann machine (RBM) [36] before performing a joint optimization of all the layers. In the following we describe the key elements of a DNN, and adopt the notation in [13]. … Cite Save

Project Periodic Report J Shawe-Taylor – 2013 – … mature to address challenging control problems that arise in the context of intelligent cognitive systems, such as home robotics, swarm intelligence, smart human-machine interfaces and dialogue systems. … The Shape Boltzmann Machine: a Strong Model of Object Shape. … Related articles Cite Save More

Language Independent Search in MediaEval’s Spoken Web Search Task F Metze, X Anguera, E Barnard, M Davel… – Computer Speech & …, 2014 – Elsevier … cases. These include searching large archives of audio-visual material, dialog systems for access to personal information and (mobile) web search, as well as applications in language learning and pronunciation training. A … Cited by 1 Related articles All 2 versions Cite Save

Tensor deep stacking networks B Hutchinson, L Deng, D Yu – Pattern Analysis and Machine …, 2013 – … called the deep convex network since learning the upper layer weights of each block could be formulated as solving a convex optimization problem with a closed-form solution, after having initialized the lower layer weights of each block with a fixed restricted Boltzmann machine … Cited by 15 Related articles All 14 versions Cite Save

2013 Index IEEE Transactions on Audio, Speech, and Language Processing Vol. 21 TD Abhayapala, C Agon, A Ahlen, S Ahmed, MT Akhtar… – … D., Jeong, M., Kim, K., Ryu, S., and Lee, GG, Unsupervised Spoken Language Understanding for a Multi-Domain Dialog System; TASL Nov. … June 2013 1251-1260 Ling, Z.-H., Deng, L., and Yu, D., Modeling Spectral Envelopes Using Restricted Boltzmann Machines and Deep … Cite Save

Joint uncertainty decoding for noise robust subspace Gaussian mixture models,” L Lu, K Chin, A Ghoshal, S Renals – IEEE Transactions on Audio, …, 2013 – … to initialise deep neural network (DNN) acoustic models by means of unsupervised restricted Boltzmann machine (RBM) pretraining. … 8.4\% less time steps and 7.7\% higher reward).}, categories = {reinforcement learning, spoken dialogue systems} } @inproceedings{hochberg … Cited by 4 Related articles All 5 versions Cite Save More

Multilingual joint parsing of syntactic and semantic dependencies with a latent variable model J Henderson, P Merlo, I Titov, G Musillo – 2013 – MIT Press … 2003; Moschitti et al. 2007), and has recently been argued to be useful in machine translation and its evaluation (Wu and Fung 2009; Liu and Gildea 2010; Lo and Wu 2011; Wu et al. 2011), dialogue systems (Basili et al. 2009 … Related articles All 6 versions Cite Save

[BOOK] Introduction to Artificial Intelligence: Second PC Jackson – 2013 – Page 1. “““’ I V/4 Z -//I ” ” IAI KJW V _,/.n/n7 / . , . . I Jlmvwwnv?w/alv/I/IW/Mr/4,///4 H. ~ 7% 21 A 3 0 ; / iv H, W I ‘ F“. isew??/I/;?v?/.4”/../Wm .“ \ ‘ WW/’M/¢¢V% Page 2. DOVER SCIENCE BOOKS DE RE METALLICA, Georgius Agricola. … Related articles Cite Save

Exploring Biologically-Inspired Interactive Networks for Object Recognition M Saifullah – 2011 – Page 1. Linköping Studies in Science and Technology Thesis No. 1466 Exploring Biologically-Inspired Interactive Networks for Object Recognition by Mohammad Saifullah Submitted to Linköping Institute of Technology at Linköping … Cited by 3 Related articles All 4 versions Cite Save

Combining visual recognition and computational linguistics: linguistic knowledge for visual recognition and natural language descriptions of visual content M Rohrbach – 2014 – Page 1. Combining Visual Recognition and Computational Linguistics Linguistic Knowledge for Visual Recognition and Natural Language Descriptions of Visual Content Thesis for obtaining the title of Doctor of Engineering Science (Dr.-Ing.) … Cite Save