Boltzmann machines
E7922
energy-based model
probabilistic graphical model
stochastic neural network architecture
unsupervised learning model
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
All labels observed (10)
How this entity was disambiguated
This entity first appeared as the object of triple T93138 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Boltzmann machines Context triple: [Geoffrey Hinton, knownFor, Boltzmann machines]
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A.
Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
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B.
Lifelong Learning Machines program
The Lifelong Learning Machines program is a DARPA research initiative aimed at developing AI systems that can continuously learn and adapt from experience in dynamic, real-world environments.
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C.
SyNAPSE neuromorphic computing program
The SyNAPSE neuromorphic computing program is a DARPA initiative to develop brain-inspired electronic systems that emulate neural architectures for highly efficient, scalable cognitive computing.
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D.
Technical Committee on Robot Learning
The Technical Committee on Robot Learning is a specialized IEEE Robotics and Automation Society group that advances research and collaboration at the intersection of machine learning and robotics.
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E.
Langevin dynamics
Langevin dynamics is a stochastic approach to modeling the motion of particles in a fluid by combining deterministic forces with random thermal fluctuations, often used to simulate Brownian motion and other nonequilibrium processes.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Boltzmann machines Target entity description: Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
-
A.
Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
-
B.
Lifelong Learning Machines program
The Lifelong Learning Machines program is a DARPA research initiative aimed at developing AI systems that can continuously learn and adapt from experience in dynamic, real-world environments.
-
C.
SyNAPSE neuromorphic computing program
The SyNAPSE neuromorphic computing program is a DARPA initiative to develop brain-inspired electronic systems that emulate neural architectures for highly efficient, scalable cognitive computing.
-
D.
Technical Committee on Robot Learning
The Technical Committee on Robot Learning is a specialized IEEE Robotics and Automation Society group that advances research and collaboration at the intersection of machine learning and robotics.
-
E.
Langevin dynamics
Langevin dynamics is a stochastic approach to modeling the motion of particles in a fluid by combining deterministic forces with random thermal fluctuations, often used to simulate Brownian motion and other nonequilibrium processes.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
energy-based model
ⓘ
probabilistic graphical model ⓘ stochastic neural network architecture ⓘ unsupervised learning model ⓘ |
| approximationMethod |
mean-field approximation
ⓘ
variational inference ⓘ |
| basedOn |
Boltzmann distribution
ⓘ
statistical mechanics ⓘ |
| definesProbability | P(s) = exp(-E(s))/Z ⓘ |
| difficulty | partition function computation is exponential in number of units ⓘ |
| hasComponent |
bias parameters
ⓘ
hidden units ⓘ visible units ⓘ |
| hasConnectionType | fully connected between all units in general form ⓘ |
| hasEnergyFunctionForm | E(v,h) = -∑_i a_i v_i -∑_j b_j h_j -∑_{i,j} v_i w_{ij} h_j -∑_{i<k} v_i u_{ik} v_k -∑_{j<l} h_j v_{jl} h_l ⓘ |
| hasLearningRule |
contrastive divergence approximation
ⓘ
persistent contrastive divergence ⓘ stochastic gradient descent on log-likelihood ⓘ |
| hasNetworkType | recurrent neural network ⓘ |
| hasPartitionFunction | Z = ∑_s exp(-E(s)) ⓘ |
| hasProperty |
Gibbs distribution over states
ⓘ
Markov random field structure ⓘ asynchronous stochastic updates ⓘ binary-valued units ⓘ converges to thermal equilibrium distribution ⓘ energy function ⓘ intractable exact learning for large networks ⓘ stochastic units ⓘ symmetrical weights ⓘ undirected connections ⓘ |
| hasSamplingMethod |
Gibbs sampling
ⓘ
Markov chain Monte Carlo ⓘ |
| inspired |
Deep Boltzmann machines
ⓘ
linked to:
Boltzmann machines
Deep belief networks ⓘ Restricted Boltzmann machines ⓘ
linked to:
Boltzmann machines
|
| introducedBy |
Geoffrey Hinton
ⓘ
Terrence Sejnowski ⓘ |
| introducedInPublication |
Learning and Relearning in Boltzmann Machines
ⓘ
linked to:
Boltzmann machines
|
| introducedInYear | 1985 ⓘ |
| relatedTo |
Hopfield networks
ⓘ
Ising models ⓘ |
| trainingObjective | maximize data log-likelihood ⓘ |
| usedFor |
associative memory
ⓘ
combinatorial optimization ⓘ density estimation ⓘ modeling complex probability distributions ⓘ representation learning ⓘ unsupervised feature learning ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Boltzmann machines Description of subject: Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
Referenced by (13)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Boltzmann machines
linked to: Boltzmann machines
linked to: Boltzmann machines
linked to: Boltzmann machines
linked to: Boltzmann machines
subject linked to:
“A fast learning algorithm for deep belief nets”
linked to: Boltzmann machines
subject linked to:
“A fast learning algorithm for deep belief nets”
linked to: Boltzmann machines
linked to: Boltzmann machines
linked to: Boltzmann machines
subject linked to:
Hopfield networks
linked to: Boltzmann machines
linked to: Boltzmann machines