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ORCIDLawrence K. Saul
Person information
- affiliation: Flatiron Institute, New York, NY, USA
- affiliation (former): University of California, San Diego, Department of Computer Science and Engineering, La Jolla, CA, USA
- affiliation (PhD 1994): Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
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2020 – today
- 2024
[i10]Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul:
Batch and match: black-box variational inference with a score-based divergence. CoRR abs/2402.14758 (2024)- [i9]Charles C. Margossian, Loucas Pillaud-Vivien, Lawrence K. Saul:
An Ordering of Divergences for Variational Inference with Factorized Gaussian Approximations. CoRR abs/2403.13748 (2024) - 2023
- [c82]Chirag Modi, Robert M. Gower, Charles Margossian, Yuling Yao, David M. Blei, Lawrence K. Saul:
Variational Inference with Gaussian Score Matching. NeurIPS 2023 - [c81]Charles C. Margossian, Lawrence K. Saul:
The Shrinkage-Delinkage Trade-off: an Analysis of Factorized Gaussian Approximations for Variational Inference. UAI 2023: 1358-1367 - [i8]Chirag Modi
, Charles Margossian, Yuling Yao, Robert M. Gower, David M. Blei, Lawrence K. Saul:
Variational Inference with Gaussian Score Matching. CoRR abs/2307.07849 (2023) - 2022
- [j25]Louis F. DeKoven, Audrey Randall, Ariana Mirian, Gautam Akiwate, Ansel Blume, Lawrence K. Saul, Aaron Schulman, Geoffrey M. Voelker, Stefan Savage:
Measuring security practices. Commun. ACM 65(9): 93-102 (2022) - [j24]Lawrence K. Saul:
A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data. SIAM J. Math. Data Sci. 4(2): 431-463 (2022) - [j23]Lawrence K. Saul:
A geometrical connection between sparse and low-rank matrices and its application to manifold learning. Trans. Mach. Learn. Res. 2022 (2022) - 2021
- [j22]Lawrence K. Saul:
An EM Algorithm for Capsule Regression. Neural Comput. 33(1): 194-226 (2021) - [c80]Lawrence K. Saul:
An online passive-aggressive algorithm for difference-of-squares classification. NeurIPS 2021: 21426-21439 - 2020
- [c79]Alex Sanchez-Stern, Yousef Alhessi, Lawrence K. Saul, Sorin Lerner:
Generating correctness proofs with neural networks. MAPL@PLDI 2020: 1-10 - [c78]Vraj Shah, Side Li, Arun Kumar, Lawrence K. Saul:
SpeakQL: Towards Speech-driven Multimodal Querying of Structured Data. SIGMOD Conference 2020: 2363-2374
2010 – 2019
- 2019
- [c77]Andrew B. Kahng, Uday Mallappa, Lawrence K. Saul, Shangyuan Tong:
"Unobserved Corner" Prediction: Reducing Timing Analysis Effort for Faster Design Convergence in Advanced-Node Design. DATE 2019: 168-173 - [c76]Louis F. DeKoven, Audrey Randall, Ariana Mirian, Gautam Akiwate, Ansel Blume, Lawrence K. Saul, Aaron Schulman, Geoffrey M. Voelker, Stefan Savage:
Measuring Security Practices and How They Impact Security. Internet Measurement Conference 2019: 36-49 - [c75]Vraj Shah, Side Li, Kevin Yang, Arun Kumar, Lawrence K. Saul:
Demonstration of SpeakQL: Speech-driven Multimodal Querying of Structured Data. SIGMOD Conference 2019: 2001-2004 - [i7]Alex Sanchez-Stern, Yousef Alhessi, Lawrence K. Saul, Sorin Lerner:
Generating Correctness Proofs with Neural Networks. CoRR abs/1907.07794 (2019) - 2018
- [c74]Andrew B. Kahng, Uday Mallappa, Lawrence K. Saul:
Using Machine Learning to Predict Path-Based Slack from Graph-Based Timing Analysis. ICCD 2018: 603-612 - 2017
- [c73]Nadir Weibel, Purvi Desai, Lawrence K. Saul, Amarnath Gupta, Susan Little:
HIV Risk on Twitter: the Ethical Dimension of Social Media Evidence-based Prevention for Vulnerable Populations. HICSS 2017: 1-10 - [c72]Dharmil Chandarana, Vraj Shah, Arun Kumar, Lawrence K. Saul:
SpeakQL: Towards Speech-driven Multi-modal Querying. HILDA@SIGMOD 2017: 11:1-11:6 - 2016
- [j21]Kritika Singh, Shava Smallen, Sameer Tilak, Lawrence K. Saul:
Failure analysis and prediction for the CIPRES science gateway. Concurr. Comput. Pract. Exp. 28(7): 1971-1981 (2016) - [j20]Steven Hill, Zhimin Zhou, Lawrence K. Saul, Hovav Shacham:
On the (In)effectiveness of Mosaicing and Blurring as Tools for Document Redaction. Proc. Priv. Enhancing Technol. 2016(4): 403-417 (2016) - 2015
- [c71]Suqi Liu, Ian D. Foster, Stefan Savage, Geoffrey M. Voelker, Lawrence K. Saul:
Who is .com?: Learning to Parse WHOIS Records. Internet Measurement Conference 2015: 369-380 - [c70]Tristan Halvorson, Matthew F. Der, Ian D. Foster, Stefan Savage, Lawrence K. Saul, Geoffrey M. Voelker:
From .academy to .zone: An Analysis of the New TLD Land Rush. Internet Measurement Conference 2015: 381-394 - 2014
- [c69]Do-kyum Kim, Matthew F. Der, Lawrence K. Saul:
A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data. AISTATS 2014: 484-492 - [c68]David Y. Wang, Matthew F. Der, Mohammad Karami, Lawrence K. Saul, Damon McCoy, Stefan Savage, Geoffrey M. Voelker:
Search + Seizure: The Effectiveness of Interventions on SEO Campaigns. Internet Measurement Conference 2014: 359-372 - [c67]Matthew F. Der, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker:
Knock it off: profiling the online storefronts of counterfeit merchandise. KDD 2014: 1759-1768 - [i6]Do-kyum Kim, Geoffrey M. Voelker, Lawrence K. Saul:
Topic Modeling of Hierarchical Corpora. CoRR abs/1409.3518 (2014) - 2013
- [c66]Do-kyum Kim, Geoffrey M. Voelker, Lawrence K. Saul:
A Variational Approximation for Topic Modeling of Hierarchical Corpora. ICML (2) 2013: 55-63 - [c65]Xiao Ma, Peng Huang, Xinxin Jin, Pei Wang, Soyeon Park, Dongcai Shen, Yuanyuan Zhou, Lawrence K. Saul, Geoffrey M. Voelker:
eDoctor: Automatically Diagnosing Abnormal Battery Drain Issues on Smartphones. NSDI 2013: 57-70 - [i5]Michael J. Kearns, Lawrence K. Saul:
Large Deviation Methods for Approximate Probabilistic Inference. CoRR abs/1301.7392 (2013) - 2012
- [c64]Matthew F. Der, Lawrence K. Saul:
Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning. NIPS 2012: 3239-3247 - 2011
- [j19]Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker:
Learning to detect malicious URLs. ACM Trans. Intell. Syst. Technol. 2(3): 30:1-30:24 (2011) - [c63]Sushma Nagesh Bannur, Lawrence K. Saul, Stefan Savage:
Judging a site by its content: learning the textual, structural, and visual features of malicious web pages. AISec 2011: 1-10 - [c62]Do-kyum Kim, Marti Motoyama, Geoffrey M. Voelker, Lawrence K. Saul:
Topic modeling of freelance job postings to monitor web service abuse. AISec 2011: 11-20 - [c61]Lawrence K. Saul, Chih-Chieh Cheng, Fei Sha:
Online learning of large margin hidden Markov models for automatic speech recognition. MLSLP 2011 - [c60]Vijay Mahadevan, Chi Wah Wong, José Costa Pereira, Tom Liu, Nuno Vasconcelos, Lawrence K. Saul:
Maximum Covariance Unfolding : Manifold Learning for Bimodal Data. NIPS 2011: 918-926 - [c59]Laurens van der Maaten, Max Welling, Lawrence K. Saul:
Hidden-Unit Conditional Random Fields. AISTATS 2011: 479-488 - [i4]Youngmin Cho, Lawrence K. Saul:
Analysis and Extension of Arc-Cosine Kernels for Large Margin Classification. CoRR abs/1112.3712 (2011) - [i3]Youngmin Cho, Lawrence K. Saul:
Nonnegative Matrix Factorization for Semi-supervised Dimensionality Reduction. CoRR abs/1112.3714 (2011) - 2010
- [j18]Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul:
Online Learning and Acoustic Feature Adaptation in Large-Margin Hidden Markov Models. IEEE J. Sel. Top. Signal Process. 4(6): 926-942 (2010) - [j17]Youngmin Cho, Lawrence K. Saul:
Large-Margin Classification in Infinite Neural Networks. Neural Comput. 22(10): 2678-2697 (2010) - [j16]Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul:
Convex Optimizations for Distance Metric Learning and Pattern Classification [Applications Corner]. IEEE Signal Process. Mag. 27(3): 146-158 (2010) - [c58]Mehran Bozorgi, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker:
Beyond heuristics: learning to classify vulnerabilities and predict exploits. KDD 2010: 105-114 - [c57]Diane Hu, Laurens van der Maaten, Youngmin Cho, Lawrence K. Saul, Sorin Lerner:
Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development. NIPS 2010: 865-873 - [c56]Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence K. Saul, Fernando Pereira:
Exploiting Feature Covariance in High-Dimensional Online Learning. AISTATS 2010: 493-500
2000 – 2009
- 2009
- [j15]Stuart Russell, Lawrence K. Saul:
Technical perspective - The ultimate pilot program. Commun. ACM 52(7): 96 (2009) - [j14]Kilian Q. Weinberger, Lawrence K. Saul:
Distance Metric Learning for Large Margin Nearest Neighbor Classification. J. Mach. Learn. Res. 10: 207-244 (2009) - [c55]Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul:
Large-margin feature adaptation for automatic speech recognition. ASRU 2009: 87-92 - [c54]Youngmin Cho, Lawrence K. Saul:
Sparse decomposition of mixed audio signals by basis pursuit with autoregressive models. ICASSP 2009: 1705-1708 - [c53]Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul:
Matrix updates for perceptron training of continuous density hidden Markov models. ICML 2009: 153-160 - [c52]Youngmin Cho, Lawrence K. Saul:
Learning dictionaries of stable autoregressive models for audio scene analysis. ICML 2009: 169-176 - [c51]Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker:
Identifying suspicious URLs: an application of large-scale online learning. ICML 2009: 681-688 - [c50]Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul:
A fast online algorithm for large margin training of continuous density hidden Markov models. INTERSPEECH 2009: 668-671 - [c49]Diane Hu, Lawrence K. Saul:
A Probabilistic Topic Model for Unsupervised Learning of Musical Key-Profiles. ISMIR 2009: 441-446 - [c48]Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker:
Beyond blacklists: learning to detect malicious web sites from suspicious URLs. KDD 2009: 1245-1254 - [c47]Youngmin Cho, Lawrence K. Saul:
Kernel Methods for Deep Learning. NIPS 2009: 342-350 - [d1]Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker:
URL Reputation. UCI Machine Learning Repository, 2009
- 2008
- [c46]Chih-Chieh Cheng, D. Jingtong Hu, Lawrence K. Saul:
Nonnegative matrix factorization for real time musical analysis and sight-reading evaluation. ICASSP 2008: 2017-2020 - [c45]Kilian Q. Weinberger, Lawrence K. Saul:
Fast solvers and efficient implementations for distance metric learning. ICML 2008: 1160-1167 - [c44]Joshua M. Lewis, Pincelli M. Hull, Kilian Q. Weinberger, Lawrence K. Saul:
Mapping Uncharted Waters: Exploratory Analysis, Visualization, and Clustering of Oceanographic Data. ICMLA 2008: 388-395 - 2007
- [j13]Fei Sha, Yuanqing Lin, Lawrence K. Saul, Daniel D. Lee:
Multiplicative Updates for Nonnegative Quadratic Programming. Neural Comput. 19(8): 2004-2031 (2007) - [c43]Fei Sha, Lawrence K. Saul:
Comparison of Large Margin Training to Other Discriminative Methods for Phonetic Recognition by Hidden Markov Models. ICASSP (4) 2007: 313-316 - [c42]Fei Sha, Y. Albert Park, Lawrence K. Saul:
Multiplicative Updates for L1-Regularized Linear and Logistic Regression. IDA 2007: 13-24 - 2006
- [j12]Kilian Q. Weinberger, Lawrence K. Saul:
Unsupervised Learning of Image Manifolds by Semidefinite Programming. Int. J. Comput. Vis. 70(1): 77-90 (2006) - [j11]Yun Mao, Lawrence K. Saul, Jonathan M. Smith:
IDES: An Internet Distance Estimation Service for Large Networks. IEEE J. Sel. Areas Commun. 24(12): 2273-2284 (2006) - [c41]Kilian Q. Weinberger, Lawrence K. Saul:
An Introduction to Nonlinear Dimensionality Reduction by Maximum Variance Unfolding. AAAI 2006: 1683-1686 - [c40]Fei Sha, Lawrence K. Saul:
Large Margin Gaussian Mixture Modeling for Phonetic Classification and Recognition. ICASSP (1) 2006: 265-268 - [c39]Fei Sha, Lawrence K. Saul:
Large Margin Hidden Markov Models for Automatic Speech Recognition. NIPS 2006: 1249-1256 - [c38]Kilian Q. Weinberger, Fei Sha, Qihui Zhu, Lawrence K. Saul:
Graph Laplacian Regularization for Large-Scale Semidefinite Programming. NIPS 2006: 1489-1496 - [p3]Lawrence K. Saul, Kilian Q. Weinberger, Fei Sha, Jihun Ham, Daniel D. Lee:
Spectral Methods for Dimensionality Reduction. Semi-Supervised Learning 2006: 292-308 - 2005
- [c37]Jihun Ham, Daniel D. Lee, Lawrence K. Saul:
Semisupervised alignment of manifolds. AISTATS 2005: 120-127 - [c36]Kilian Q. Weinberger, Benjamin Packer, Lawrence K. Saul:
Nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization. AISTATS 2005: 381-388 - [c35]John Ashley Burgoyne, Lawrence K. Saul:
Visualization of low Dimensional Structure in tonal pitch Space. ICMC 2005 - [c34]Fei Sha, Lawrence K. Saul:
Analysis and extension of spectral methods for nonlinear dimensionality reduction. ICML 2005: 784-791 - [c33]John Ashley Burgoyne, Lawrence K. Saul:
Learning Harmonic Relationships in Digital Audio with Dirichlet-Based Hidden Markov Models. ISMIR 2005: 438-443 - [c32]Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul:
Distance Metric Learning for Large Margin Nearest Neighbor Classification. NIPS 2005: 1473-1480 - 2004
- [c31]Kilian Q. Weinberger, Lawrence K. Saul:
Unsupervised Learning of Image Manifolds by Semidefinite Programming. CVPR (2) 2004: 988-995 - [c30]Yuanqing Lin, Daniel D. Lee, Lawrence K. Saul:
Nonnegative deconvolution for time of arrival estimation. ICASSP (2) 2004: 377-380 - [c29]Fei Sha, John Ashley Burgoyne, Lawrence K. Saul:
Multiband statistical learning for f0 estimation in speech. ICASSP (5) 2004: 661-664 - [c28]Viren Jain, Lawrence K. Saul:
Exploratory analysis and visualization of speech and music by locally linear embedding. ICASSP (3) 2004: 984-987 - [c27]Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul:
Learning a kernel matrix for nonlinear dimensionality reduction. ICML 2004 - [c26]Yun Mao, Lawrence K. Saul:
Modeling distances in large-scale networks by matrix factorization. Internet Measurement Conference 2004: 278-287 - [c25]John Blitzer, Kilian Q. Weinberger, Lawrence K. Saul, Fernando Pereira:
Hierarchical Distributed Representations for Statistical Language Modeling. NIPS 2004: 185-192 - [c24]Fei Sha, Lawrence K. Saul:
Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization. NIPS 2004: 1233-1240 - [e1]Sebastian Thrun, Lawrence K. Saul, Bernhard Schölkopf:
Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada]. MIT Press 2004, ISBN 0-262-20152-6 [contents] - 2003
- [j10]Lawrence K. Saul, Sam T. Roweis:
Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold. J. Mach. Learn. Res. 4: 119-155 (2003) - [c23]Andrew I. Schein, Lawrence K. Saul, Lyle H. Ungar:
A Generalized Linear Model for Principal Component Analysis of Binary Data. AISTATS 2003: 240-247 - [c22]Fei Sha, Lawrence K. Saul, Daniel D. Lee:
Multiplicative Updates for Large Margin Classifiers. COLT 2003: 188-202 - [c21]Lawrence K. Saul, Fei Sha, Daniel D. Lee:
Statistical signal processing with nonnegativity constraints. INTERSPEECH 2003: 1001-1004 - 2002
- [c20]Fei Sha, Lawrence K. Saul, Daniel D. Lee:
Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines. NIPS 2002: 1041-1048 - [c19]Lawrence K. Saul, Daniel D. Lee, Charles L. Isbell Jr., Yann LeCun:
Real Time Voice Processing with Audiovisual Feedback: Toward Autonomous Agents with Perfect Pitch. NIPS 2002: 1181-1188 - 2001
- [j9]Lawrence K. Saul, Mazin G. Rahim, Jont B. Allen:
A statistical model for robust integration of narrowband cues in speech. Comput. Speech Lang. 15(2): 175-194 (2001) - [j8]Mazin G. Rahim, Giuseppe Riccardi, Lawrence K. Saul, Jeremy H. Wright, Bruce Buntschuh, Allen L. Gorin:
Robust numeric recognition in spoken language dialogue. Speech Commun. 34(1-2): 195-212 (2001) - [c18]Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinton:
Global Coordination of Local Linear Models. NIPS 2001: 889-896 - [c17]Lawrence K. Saul, Daniel D. Lee:
Multiplicative Updates for Classification by Mixture Models. NIPS 2001: 897-904 - 2000
- [j7]Lawrence K. Saul, Mazin G. Rahim:
Markov Processes on Curves. Mach. Learn. 41(3): 345-363 (2000) - [j6]Lawrence K. Saul, Michael I. Jordan:
Attractor Dynamics in Feedforward Neural Networks. Neural Comput. 12(6): 1313-1335 (2000) - [j5]Lawrence K. Saul, Mazin G. Rahim:
Maximum likelihood and minimum classification error factor analysis for automatic speech recognition. IEEE Trans. Speech Audio Process. 8(2): 115-125 (2000) - [c16]Lawrence K. Saul, Jont B. Allen:
Periodic Component Analysis: An Eigenvalue Method for Representing Periodic Structure in Speech. NIPS 2000: 807-813
1990 – 1999
- 1999
- [j4]Lawrence K. Saul, Michael I. Jordan:
Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones. Mach. Learn. 37(1): 75-87 (1999) - [j3]Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul:
An Introduction to Variational Methods for Graphical Models. Mach. Learn. 37(2): 183-233 (1999) - [c15]Lawrence K. Saul, Mazin G. Rahim:
Modeling the rate of speech by Markov processes on curves. EUROSPEECH 1999: 415-418 - 1998
- [c14]Lawrence K. Saul:
Automatic Segmentation of Continuous Trajectories with Invariance to Nonlinear Warpings of Time. ICML 1998: 506-514 - [c13]Michael J. Kearns, Lawrence K. Saul:
Inference in Multilayer Networks via Large Deviation Bounds. NIPS 1998: 260-266 - [c12]Lawrence K. Saul, Mazin G. Rahim:
Markov Processes on Curves for Automatic Speech Recognition. NIPS 1998: 751-760 - [c11]Michael J. Kearns, Lawrence K. Saul:
Large Deviation Methods for Approximate Probabilistic Inference. UAI 1998: 311-319 - [p2]Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul:
An Introduction to Variational Methods for Graphical Models. Learning in Graphical Models 1998: 105-161 - [p1]Lawrence K. Saul, Michael I. Jordan:
A Mean Field Learning Algorithm for Unsupervised Neural Networks. Learning in Graphical Models 1998: 541-554 - 1997
- [c10]Lawrence K. Saul, Michael I. Jordan:
Mixed Memory Markov Models. AISTATS 1997: 437-444 - [c9]Lawrence K. Saul, Fernando Pereira:
Aggregate and mixed-order Markov models for statistical language processing. EMNLP 1997 - [c8]Lawrence K. Saul, Mazin G. Rahim:
Modeling Acoustic Correlations by Factor Analysis. NIPS 1997: 749-755 - [i2]Lawrence K. Saul, Fernando Pereira:
Aggregate and mixed-order Markov models for statistical language processing. CoRR cmp-lg/9706007 (1997) - 1996
- [j2]Lawrence K. Saul, Tommi S. Jaakkola, Michael I. Jordan:
Mean Field Theory for Sigmoid Belief Networks. J. Artif. Intell. Res. 4: 61-76 (1996) - [c7]Lawrence K. Saul, Satinder P. Singh:
Learning Curve Bounds for a Markov Decision Process with Undiscounted Rewards. COLT 1996: 147-156 - [c6]Lawrence K. Saul, Michael I. Jordan:
A Variational Principle for Model-based Morphing. NIPS 1996: 267-273 - [c5]Michael I. Jordan, Zoubin Ghahramani, Lawrence K. Saul:
Hidden Markov Decision Trees. NIPS 1996: 501-507 - [i1]Lawrence K. Saul, Tommi S. Jaakkola, Michael I. Jordan:
Mean Field Theory for Sigmoid Belief Networks. CoRR cs.AI/9603102 (1996) - 1995
- [c4]Lawrence K. Saul, Satinder P. Singh:
Markov Decision Processes in Large State Spaces. COLT 1995: 281-288 - [c3]Lawrence K. Saul, Michael I. Jordan:
Exploiting Tractable Substructures in Intractable Networks. NIPS 1995: 486-492 - [c2]Tommi S. Jaakkola, Lawrence K. Saul, Michael I. Jordan:
Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. NIPS 1995: 528-534 - 1994
- [j1]Lawrence K. Saul, Michael I. Jordan:
Learning in Boltzmann Trees. Neural Comput. 6(6): 1174-1184 (1994) - [c1]Lawrence K. Saul, Michael I. Jordan:
Boltzmann Chains and Hidden Markov Models. NIPS 1994: 435-442
Coauthor Index
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