The DOI matches its Crossref record; 29 of the 30 references it cites were found in a registry or an index.
A learning algorithm for Boltzmann machines
Abstract
The computational power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections can allow a significant fraction of the knowledge of the system to be applied to an instance of a problem in a very short time. One kind of computation for which massively parallel networks appear to be well suited is large constraint satisfaction searches, but to use the connections efficiently two conditions must be met: First, a search technique that is suitable for parallel networks must be found. Second, there must be some way of choosing internal representations which allow the preexisting hardware connections to be used efficiently for encoding the constraints in the domain being searched. We describe a general parallel search method, based on statistical mechanics, and we show how it leads to a general learning rule for modifying the connection strengths so as to incorporate knowledge about a task domain in an efficient way. We describe some simple examples in which the learning algorithm creates internal representations that are demonstrably the most efficient way of using the preexisting connectivity structure.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“It would take another 24 years before the connectionist research community invented a learning algorithm for multilayer networks of stochastic binary neurons (Ackley et al., 1985) and rediscovered the backpropagation algorithm for deterministic, real-valued neurons and applied them to neural networks (Rumelhart et al., 1986).”
From Large Language Models and the Reverse Turing Test (2023) · cites this paper as Ackley et al., 1985
In the citing paper
“The quantum Boltzmann machine (Amin et al. , 2018) (QBM) extends the classical Boltzmann machine (Ackley et al. , 1985), a neural architecture capable of several tasks including generative modeling of data.”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Ackley et al. , 1985
In the citing paper
“For many classes of generative models, there is a much simpler way to reduce diversity: reducing the temperature [1].”
From Diffusion Models Beat GANs on Image Synthesis (2021) · cites this paper as [1]
In the citing paper
“Moreover, neural networks dating back to the Boltzmann machine [87] take a sampling approach to probabilistic inference.”
From Bayesian Brains without Probabilities (2016) · cites this paper as [87]
In the citing paper
“As shown below, these spiking network models can emulate any Boltzmann machine (BM) [36].”
From Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons (2011) · cites this paper as [36]
In the citing paper
“Other classical methods based on machine learning and Markov Chain Monte Carlo, such as the Boltzmann machine, 55 could achieve higher accuracy by requiring more computational resources than the inverse Bethe approximation used here.”
From A generative modeling approach for benchmarking and training shallow quantum circuits (2019) · cites this paper as [55]
In the citing paper
“This illustrates the power of Born machines 24 to approximate Boltzmann machines 55 when the data require thermal-like features.”
From A generative modeling approach for benchmarking and training shallow quantum circuits (2019) · cites this paper as [55]
In the citing paper
“Originally named Harmonium by Smolensky, RBM is a variant of Boltzmann machines [2] with the restriction that there are no connections within any of the network layers, whether it is visible or hidden.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [2]
Citations
Cites
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Information theory and statistics
(1959) · Journal of the Franklin Institute · doi:10.1016/0016-0032(59)90368-0 · Cited by 7,318
Axten, N., Newell, A., & Simon, H. A. (1973) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2063712 · Cited by 6,886
Ratcliff, R. (1978) · Psychological Review · doi:10.1037/0033-295x.85.2.59 · Cited by 4,513
The psychology of computer vision
Rosenfeld, A. (1976) · Artificial Intelligence · doi:10.1016/0004-3702(76)90008-4 · Cited by 3,015
Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms
White, B. W., & Rosenblatt, F. (1963) · The American Journal of Psychology · doi:10.2307/1419730 · Cited by 1,818
PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS
ROSENBLATT, F. (1961) · doi:10.21236/ad0256582 · Cited by 1,691
Research works printed but not linked: 9
- Berliner H. J., 1982, Proceedings of the National Conference on Artificial Intelligence AAAI‐82, 213
- Binder K., 1978, The Monte‐Carlo method in statistical physics
- Hinton G. E., 1977, Computer Vision, 408
- Hinton G. E., 1983, Proceedings of the Fifth Annual Conference of the Cognitive Science Society
- Hinton G. E., 1984, Boltzmann Machines: Constraint satisfaction networks that learn
- Minsky M., 1968, Perceptrons
- Renyi A., 1962, Probability theory
- Smolensky P., 1983, Proceedings of the National Conference on Artificial Intelligence AAAI‐83, 109
- Winston P. H., 1984, Artificial Intelligence
Cited by
Sorted by citations
Items 201–300 of 1,013
Machine learning for comprehensive forecasting of Alzheimer’s Disease progression
Fisher, C. K., Smith, A. M., Walsh, J. R., Diseases, C. A. M., Simon, A. J., Edgar, C., Jack, C. R., Holtzman, D., Russell, D., Hill, D., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-49656-2 · Cited by 223
Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance PrinciplesPreprint
Ollivier, Y., Arnold, L., Auger, A., & Hansen, N. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1106.3708 · Cited by 221
Landauer, T. K. (1986) · Cognitive Science · doi:10.1207/s15516709cog1004_4 · Cited by 226
A Connectionist/Control Architecture for Working Memory
Schneider, W., & Detweiler, M. C. (1988) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/s0079-7421(08)60026-2 · Cited by 223
Massively parallel probabilistic computing with sparse Ising machines
Aadit, N. A., Grimaldi, A., Carpentieri, M., Theogarajan, L. S., Martinis, J. M., Finocchio, G., & Camsari, K. Y. (2022) · Nature Electronics · doi:10.1038/s41928-022-00774-2 · Cited by 219
Avoiding false local minima by proper initialization of connections
Wessels, L. F. A., & Barnard, E. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.165592 · Cited by 219
A neural network model of retrieval-induced forgetting
Norman, K. A., Newman, E. L., & Detre, G. (2007) · Psychological Review · doi:10.1037/0033-295x.114.4.887 · Cited by 218
Reasoning with Language Model Prompting: A Survey
Qiao, S., Ou, Y., Zhang, N., Chen, X., Yao, Y., Deng, S., Tan, C., Huang, F., & Chen, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.294 · Cited by 346
Supervised Learning of Image Restoration with Convolutional Networks
Jain, V., Murray, J. F., Roth, F., Turaga, S., Zhigulin, V., Briggman, K. L., Helmstaedter, M. N., Denk, W., & Seung, H. S. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408909 · Cited by 217
Gotts, S. J., Chow, C. C., & Martin, A. (2012) · Cognitive Neuroscience · doi:10.1080/17588928.2012.670617 · Cited by 213
eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates
Pu, L., Naderi, M., Liu, T., Wu, H.-C., Mukhopadhyay, S., & Brylinski, M. (2019) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-018-0282-6 · Cited by 212
The effect of sampling temperature on problem solving in large language models
Renze, M. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024 · doi:10.18653/v1/2024.findings-emnlp.432 · Cited by 212
Aarts, E., van der Horn, P., Korst, J., Michiels, W., & Sontrop, H. (2003) · Princeton University Press eBooks · doi:10.1007/0-387-28356-0_7 · Cited by 222
Integration of acoustic and visual speech signals using neural networks
Yuhas, B. P., Goldstein, M. H., & Sejnowski, T. J. (1989) · IEEE Communications Magazine · doi:10.1109/35.41402 · Cited by 209
Roudi, Y., Tyrcha, J., & Hertz, J. (2009) · Physical Review E · doi:10.1103/physreve.79.051915 · Cited by 208
Emergent Criticality in Complex Turing B‐Type Atomic Switch Networks
Stieg, A. Z., Avizienis, A. V., Sillin, H. O., Martin‐Olmos, C., Aono, M., & Gimzewski, J. K. (2011) · Advanced Materials · doi:10.1002/adma.201103053 · Cited by 212
Wu, L. Y., Shen, C., & van den Hengel, A. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.12.022 · Cited by 208
A review on computational intelligence for identification of nonlinear dynamical systems
Quaranta, G., Lacarbonara, W., & Masri, S. F. (2020) · Nonlinear Dynamics · doi:10.1007/s11071-019-05430-7 · Cited by 215
Kumar, V., Azamathulla, H. M., Sharma, K. V., Mehta, D., & Tota‐Maharaj, K. (2023) · Sustainability · doi:10.3390/su151310543 · Cited by 217
Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters
Li, Y., Chen, C., & Wasserman, W. W. (2016) · Journal of Computational Biology · doi:10.1089/cmb.2015.0189 · Cited by 215
Deep learning techniques for hyperspectral image analysis in agriculture: A review
Guerri, M. F., Distante, C., Spagnolo, P., Bougourzi, F., & Ahmed, A. T. (2024) · ISPRS Open Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.ophoto.2024.100062 · Cited by 220
Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect
Gruau, F., & Whitley, D. (1993) · Evolutionary Computation · doi:10.1162/evco.1993.1.3.213 · Cited by 206
The Fusion of Deep Learning and Fuzzy Systems: A State-of-the-Art Survey
Zheng, Y., Xu, Z., & Wang, X. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3062899 · Cited by 209
Generative AI in Higher Education: The ChatGPT Effect
Chan, C. K. Y., & Colloton, T. (2024) · Routledge · doi:10.4324/9781003459026 · Cited by 203
Restricted Boltzmann machines in quantum physics
Melko, R. G., Carleo, G., Carrasquilla, J., & Cirac, J. I. (2019) · Nature Physics · doi:10.1038/s41567-019-0545-1 · Cited by 215
List-strength effect: II. Theoretical mechanisms.
Shiffrin, R. M., Ratcliff, R., & Clark, S. E. (1990) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037//0278-7393.16.2.179 · Cited by 207
Volponi, A. J., DePold, H. R., Ganguli, R., & Daguang, C. (2003) · Journal of Engineering for Gas Turbines and Power · doi:10.1115/1.1419016 · Cited by 202
Quantum variational autoencoder
Khoshaman, A., Vinci, W., Denis, B., Andriyash, E., Sadeghi, H., & Amin, M. H. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aada1f · Cited by 218
A survey of swarm and evolutionary computing approaches for deep learning
Darwish, A., Hassanien, A. E., & Das, S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09719-2 · Cited by 200
Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments
Novaes, M. P., Carvalho, L. F., Lloret, J., & Proença, M. L. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.06.047 · Cited by 197
Noise as a Resource for Computation and Learning in Networks of Spiking Neurons
Maass, W. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2310593 · Cited by 195
Equivalence of Backpropagation and Contrastive Hebbian Learning in a Layered Network
Xie, X. S., & Seung, H. S. (2003) · Neural Computation · doi:10.1162/089976603762552988 · Cited by 200
Analogue spin–orbit torque device for artificial-neural-network-based associative memory operation
Borders, W. A., Akima, H., Fukami, S., Moriya, S., Kurihara, S., Horio, Y., Sato, S., & Ohno, H. (2016) · Applied Physics Express · doi:10.7567/apex.10.013007 · Cited by 192
Information geometry of Boltzmann machines
Амари, Ш.-И., Kurata, K., & Nagaoka, H. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.125867 · Cited by 189
Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks
Song, A., Choi, J., Han, Y., & Kim, Y. (2018) · Remote Sensing · doi:10.3390/rs10111827 · Cited by 190
Learning algorithms and probability distributions in feed-forward and feed-back networks
Hopfield, J. J. (1987) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.84.23.8429 · Cited by 192
Cerebral cortex : principles of operation
Rolls, E. T. (2016) · doi:10.82444/warw.33712699 · Cited by 187
Optimization strategies gleaned from biological evolution
Brady, R. M. (1985) · Nature · doi:10.1038/317804a0 · Cited by 190
Learning state space trajectories in recurrent neural networks
Pearlmutter, B. A. (1989) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1989.118724 · Cited by 187
Implicit Generation and Modeling in Energy-Based ModelsPreprint
Du, Y., & Mordatch, I. (2019) · arXiv (Cornell University) · Cited by 187
Efficient Learning in Boltzmann Machines Using Linear Response Theory
Kappen, H. J., & Rodríguez, F. B. (1998) · Neural Computation · doi:10.1162/089976698300017386 · Cited by 187
Rigotti, M. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00024 · Cited by 184
Ethics and responsible AI deployment
Radanliev, P., Santos, O., Brandon‐Jones, A., & Joinson, A. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1377011 · Cited by 182
Query optimization by simulated annealing
Ioannidis, Y., & Wong, E. (1987) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/38713.38722 · Cited by 180
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and ArchitecturesPreprint
Bartunov, S., Santoro, A., Richards, B. A., Marris, L., Hinton, G. E., & Lillicrap, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.04587 · Cited by 196
Self-citationGeneral-Purpose Computation with Neural Networks: A Survey of Complexity Theoretic Results
Šı́ma, J., & Orponen, P. (2003) · Neural Computation · doi:10.1162/089976603322518731 · Cited by 179
Survey of Deep Representation Learning for Speech Emotion Recognition
Latif, S., Rana, R. K., Khalifa, S., Jurdak, R., Qadir, J., & Schuller, B. W. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3114365 · Cited by 193
Learning translation invariant recognition in a massively parallel networks
Hinton, G. E. (1987) · Lecture notes in computer science · doi:10.1007/3-540-17943-7_117 · Cited by 179
Self-citationWhat connectionist models learn: Learning and representation in connectionist networks
Hanson, S. J., & Burr, D. J. (1990) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00079760 · Cited by 180
Methodologies for studying human knowledge
Anderson, J. R. (1987) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00023554 · Cited by 177
Intelligent and connected vehicles: Current status and future perspectives
Yang, D., Jiang, K., Zhao, D. X., Yu, C., Cao, Z. L., Xie, S., Xiao, Z., Jiao, X., Wang, S., & Zhang, K. (2018) · Science China Technological Sciences · doi:10.1007/s11431-017-9338-1 · Cited by 183
Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs
Hsiao, W.-Y., Liu, J.-Y., Yeh, Y.-C., & Yang, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16091 · Cited by 199
Deep generative models: Survey
Oussidi, A., & Elhassouny, A. (2018) · International Conference on Intelligent Systems and Computer Vision (ISCV) · doi:10.1109/isacv.2018.8354080 · Cited by 190
Reducing data dimensionality through optimizing neural network inputs
Tan, S., & Mayrovouniotis, M. L. (1995) · AIChE Journal · doi:10.1002/aic.690410612 · Cited by 175
Symbols among the neurons: details of a connectionist inference architecture
Touretzky, D. S., & Minton, G. E. (1985) · International Joint Conference on Artificial Intelligence · Cited by 175
Some extensions of score matching
Hyvärinen, A. (2006) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.09.003 · Cited by 174
Homeostasis and soft robotics in the design of feeling machines
Man, K., & Damásio, A. R. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0103-7 · Cited by 174
Lightweight Probabilistic Deep Networks
Gast, J., & Roth, S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00355 · Cited by 178
Improved Learning of Gaussian-Bernoulli Restricted Boltzmann Machines
Cho, K., Ilin, A., & Raiko, T. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21735-7_2 · Cited by 172
Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method
Jia, Y., Wu, J., & Xu, M. (2017) · Journal of Advanced Transportation · doi:10.1155/2017/6575947 · Cited by 175
The Wilson–Cowan model, 36 years later
Alain Destexhe, A., & B, T. J. S. A. (2009) · Biological Cybernetics · doi:10.1007/s00422-009-0328-3 · Cited by 176
Adaptive networks for fault diagnosis and process control
Ungar, L., Powell, B. A., & Kamens, S. N. (1990) · Computers & Chemical Engineering · doi:10.1016/0098-1354(90)87027-m · Cited by 173
Large Language Models and the Reverse Turing Test
Sejnowski, T. J. (2023) · Neural Computation · doi:10.1162/neco_a_01563 · Cited by 169
Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet
Rolls, E. T. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00035 · Cited by 174
Cramer, A. O. J., Waldorp, L., van der Maas, H. L. J., & Borsboom, D. (2010) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10000920 · Cited by 170
A Modular Neural Network Model of Concept Acquisition
Schyns, P. G. (1991) · Cognitive Science · doi:10.1207/s15516709cog1504_1 · Cited by 167
Implicit Generation and Generalization in Energy-Based ModelsPreprint
Du, Y., & Mordatch, I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.08689 · Cited by 167
Karimi, M., Majidi, M., Mirsaeedi, H., Arefi, M. M., & Oskuoee, M. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2908580 · Cited by 166
Neural net algorithms that learn in polynomial time from examples and queries
Baum, E. B. (1991) · IEEE Transactions on Neural Networks · doi:10.1109/72.80287 · Cited by 166
From recurrent choice to skill learning: A reinforcement-learning model.
Fu, W., & Anderson, J. R. (2006) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.135.2.184 · Cited by 168
A connectionist account of asymmetric category learning in early infancy.
Mareschal, D., French, R. M., & Quinn, P. C. (2000) · Developmental Psychology · doi:10.1037/0012-1649.36.5.635 · Cited by 165
Hippocampal remapping as hidden state inference
Sanders, H., Wilson, M. A., & Gershman, S. J. (2020) · eLife · doi:10.7554/elife.51140 · Cited by 163
Using artificial neural networks for solving chemical problems
Smits, J. R. M., Melssen, W. J., Buydens, L. M. C., & Kateman, G. (1994) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(93)e0035-3 · Cited by 163
Inverse Ising Inference Using All the Data
Aurell, E., & Ekeberg, M. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.090201 · Cited by 162
Maximum likelihood neural networks for sensor fusion and adaptive classification
PERLOVSKY, L. I., & McManus, M. M. (1991) · Neural Networks · doi:10.1016/0893-6080(91)90035-4 · Cited by 162
Zhou, N.-R., Zhang, T.-F., Xie, X.-W., & Wu, J.-Y. (2023) · Signal Processing Image Communication · doi:10.1016/j.image.2022.116891 · Cited by 161
Models For Recall And Recognition
Raaijmakers, J. G. W., & Shiffrin, R. M. (1992) · Annual Review of Psychology · doi:10.1146/annurev.ps.43.020192.001225 · Cited by 164
Divergence Function, Duality, and Convex Analysis
Zhang, J. (2004) · Neural Computation · doi:10.1162/08997660460734047 · Cited by 159
State and pressures of the marine and coastal Mediterranean environment
Map, U. (2016) · Cited by 159
Intentionality and information processing: An alternative model for cognitive science
Sayre, K. M. (1986) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00021750 · Cited by 159
Generic constraints on underspecified target trajectories
Jordan, M. I. (1989) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1989.118584 · Cited by 158
Freeway Work Zone Traffic Delay and Cost Optimization Model
Jiang, X., & Adeli, H. (2003) · Journal of Transportation Engineering · doi:10.1061/(asce)0733-947x(2003)129:3(230 · Cited by 157
Improving Generative Adversarial Networks with Denoising Feature Matching
Warde-Farley, D., & Bengio, Y. (2017) · International Conference on Learning Representations · Cited by 157
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
Benedetti, M., Realpe-Gómez, J., Biswas, R., & Perdomo‐Ortiz, A. (2017) · Physical Review X · doi:10.1103/physrevx.7.041052 · Cited by 172
Enhancing discrete choice models with representation learning
Sifringer, B., Lurkin, V., & Alahi, A. (2020) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2020.08.006 · Cited by 180
The Selection of Syntactic Knowledge
Clark, R. (1992) · Language Acquisition · doi:10.1207/s15327817la0202_1 · Cited by 153
Sparse Neural Networks With Large Learning Diversity
Gripon, V., & Berrou, C. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2146789 · Cited by 154
Mean-field theory of Boltzmann machine learning
Tanaka, T. (1998) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.58.2302 · Cited by 150
Précis ofSemantic Cognition: A Parallel Distributed Processing Approach
Rogers, T. T., & McClelland, J. L. (2008) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0800589x · Cited by 161
A Connectionist Approach to Knowledge Representation and Limited Inference
Shastri, L. (1988) · Cognitive Science · doi:10.1016/0364-0213(88)90027-4 · Cited by 151
The Emergent neural modeling system
Aisa, B., Mingus, B., & O'Reilly, R. (2008) · Neural Networks · doi:10.1016/j.neunet.2008.06.016 · Cited by 152
The connectivity of the brain: multi-level quantitative analysis
Murre, J. M. J., & Sturdy, D. P. F. (1995) · Biological Cybernetics · doi:10.1007/bf00199545 · Cited by 150
Stochastic Memory Devices for Security and Computing
Carboni, R., & Ielmini, D. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900198 · Cited by 150
New Method for Parameter Estimation in Probabilistic Models: Minimum Probability Flow
Sohl‐Dickstein, J., Battaglino, P., & DeWeese, M. R. (2011) · Physical Review Letters · doi:10.1103/physrevlett.107.220601 · Cited by 146
Feature Extraction With Deep Neural Networks by a Generalized Discriminant Analysis
Stuhlsatz, A., Lippel, J., & Zielke, T. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2183645 · Cited by 146
Connectionist Architectures for Artificial Intelligence
Fahlman, & Hinton (1987) · Computer · doi:10.1109/mc.1987.1663364 · Cited by 148
Distributed Winner-Take-All in Dynamic Networks
Li, S., Zhou, M., Luo, X., & You, Z. (2016) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2016.2578645 · Cited by 145
The Modern Mathematics of Deep Learning
Berner, J., Grohs, P., Gitta Kutyniok, B., & Petersen, P. (2022) · Cambridge University Press eBooks · doi:10.1017/9781009025096.002 · Cited by 199
Efficiently Learning Ising Models on Arbitrary Graphs
Bresler, G. (2015) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/2746539.2746631 · Cited by 143
Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data
Cocco, S., & Monasson, R. (2011) · Physical Review Letters · doi:10.1103/physrevlett.106.090601 · Cited by 142
Metadata
- Authors
- Ackley, D. H., Hinton, G. E., & Sejnowski, T. J.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Ackley, David H.
- ORCID
- Ackley, David H.
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 1985-01
- Volume
- 9
- Issue
- 1
- Pages
- 147-169
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W2042492924
- Cited by
- 3,402
- References
- 30
- Citations by year
- 2026: 21 · 2025: 72 · 2024: 99 · 2023: 141 · 2022: 158 · 2021: 204 · 2020: 169 · 2019: 178 · 2018: 162 · 2017: 140 · 2016: 118 · 2015: 102 · 2014: 86 · 2013: 102 · 2012: 90
- FWCI
- 122.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cognitive Science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- Boltzmann machine, learning algorithms, constraint satisfaction, parallel search, statistical mechanics, internal representations, constraint encoding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
