MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES
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[1] A. Wald. Contributions to the Theory of Statistical Estimation and Testing Hypotheses , 1939 .
[2] Dr. M. G. Worster. Methods of Mathematical Physics , 1947, Nature.
[3] Le Cam,et al. On some asymptotic properties of maximum likelihood estimates and related Bayes' estimates , 1953 .
[4] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[5] O. Schade. Optical and photoelectric analog of the eye. , 1956, Journal of the Optical Society of America.
[6] J. Lamperti. ON CONVERGENCE OF STOCHASTIC PROCESSES , 1962 .
[7] A Tikhonov,et al. Solution of Incorrectly Formulated Problems and the Regularization Method , 1963 .
[8] R. Newton. Scattering theory of waves and particles , 1966 .
[9] J. Robson,et al. Application of fourier analysis to the visibility of gratings , 1968, The Journal of physiology.
[10] L. Baum,et al. A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .
[11] G. Wahba,et al. A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines , 1970 .
[12] K. B. Haley,et al. Optimization Theory with Applications , 1970 .
[13] N. Goldenfeld. Lectures On Phase Transitions And The Renormalization Group , 1972 .
[14] M. Stone. Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .
[15] David M. Allen,et al. The Relationship Between Variable Selection and Data Agumentation and a Method for Prediction , 1974 .
[16] A. Tversky,et al. Judgment under Uncertainty: Heuristics and Biases , 1974, Science.
[17] Noam Chomsky,et al. The Logical Structure of Linguistic Theory , 1975 .
[18] C. WEHRHAHN,et al. Real-time delayed tracking in flies , 1976, Nature.
[19] D. W. Noid. Studies in Molecular Dynamics , 1976 .
[20] M. Stone. An Asymptotic Equivalence of Choice of Model by Cross‐Validation and Akaike's Criterion , 1977 .
[21] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[22] R. F. Hess,et al. The threshold contrast sensitivity function in strabismic amblyopia: Evidence for a two type classification , 1977, Vision Research.
[23] Abraham Lempel,et al. Compression of individual sequences via variable-rate coding , 1978, IEEE Trans. Inf. Theory.
[24] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[25] Akira Kodama. Mechanism of Color Perception , 1979 .
[26] J. Baker. Trainable grammars for speech recognition , 1979 .
[27] L. Schumaker. Spline Functions: Basic Theory , 1981 .
[28] A. Cohen,et al. Finite Mixture Distributions , 1982 .
[29] J. Gerard Wolff,et al. Language acquisition, data compression and generalization , 1982 .
[30] Abraham Kandel,et al. Fuzzy techniques in pattern recognition , 1982 .
[31] Geoffrey E. Hinton,et al. OPTIMAL PERCEPTUAL INFERENCE , 1983 .
[32] Morris Halle,et al. On distinctive features and their articulatory implementation , 1983 .
[33] W. Nelson Francis,et al. FREQUENCY ANALYSIS OF ENGLISH USAGE: LEXICON AND GRAMMAR , 1983 .
[34] G. Wahba. Bayesian "Confidence Intervals" for the Cross-validated Smoothing Spline , 1983 .
[35] Raymond A. DeCarlo,et al. Continuation methods: Theory and applications , 1983, IEEE Transactions on Systems, Man, and Cybernetics.
[36] R. Dudley. A course on empirical processes , 1984 .
[37] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[38] Shun-ichi Amari,et al. Differential-geometrical methods in statistics , 1985 .
[39] C Snow,et al. Child language data exchange system , 1984, Journal of Child Language.
[40] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..
[41] A. F. Smith,et al. Statistical analysis of finite mixture distributions , 1986 .
[42] Geoffrey E. Hinton,et al. Learning and relearning in Boltzmann machines , 1986 .
[43] L. L. Cam,et al. Asymptotic Methods In Statistical Decision Theory , 1986 .
[44] A. Verri,et al. Regularization Theory and Shape Constraints , 1986 .
[45] D. Amit,et al. Statistical mechanics of neural networks near saturation , 1987 .
[46] Sergei Ovchinnikov,et al. Fuzzy sets and applications , 1987 .
[47] Richard Durbin,et al. An analogue approach to the travelling salesman problem using an elastic net method , 1987, Nature.
[48] Lawrence Davis,et al. Genetic Algorithms and Simulated Annealing , 1987 .
[49] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[50] Judea Pearl,et al. Probabilistic reasoning in intelligent systems , 1988 .
[51] P. J. Green,et al. Density Estimation for Statistics and Data Analysis , 1987 .
[52] J. Berger. Statistical Decision Theory and Bayesian Analysis , 1988 .
[53] Stéphane Mallat,et al. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..
[54] F. Girosi,et al. Networks for approximation and learning , 1990, Proc. IEEE.
[55] A. Sitenko,et al. DIRECT NUCLEAR REACTIONS , 1990 .
[56] Rose,et al. Statistical mechanics and phase transitions in clustering. , 1990, Physical review letters.
[57] D. J. Wallace. Statistical field theory. Volumes 1 and 2 , 1990 .
[58] G. Wahba. Spline models for observational data , 1990 .
[59] Alan L. Yuille,et al. Generalized Deformable Models, Statistical Physics, and Matching Problems , 1990, Neural Computation.
[60] Tomaso A. Poggio,et al. Extensions of a Theory of Networks for Approximation and Learning , 1990, NIPS.
[61] Adrian F. M. Smith,et al. Sampling-Based Approaches to Calculating Marginal Densities , 1990 .
[62] S. E. Hills,et al. Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling , 1990 .
[63] Grace L. Yang,et al. Asymptotics In Statistics , 1990 .
[64] Grace Wahba,et al. Spline Models for Observational Data , 1990 .
[65] Yaser S. Abu-Mostafa,et al. Learning from hints in neural networks , 1990, J. Complex..
[66] R. Bishop,et al. Quantum many-particle systems , 1990 .
[67] Michel Le Bellac,et al. Quantum and statistical field theory , 1991 .
[68] R. Balian. From microphysics to macrophysics , 1991 .
[69] Anders Krogh,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[70] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[71] Andrew L. Rukhin,et al. Tools for statistical inference , 1991 .
[72] Jocelyn Sietsma,et al. Creating artificial neural networks that generalize , 1991, Neural Networks.
[73] M. Ferraro. Invariant Pattern Representations and Lie Groups Theory , 1992 .
[74] Fernando Pereira,et al. Inside-Outside Reestimation From Partially Bracketed Corpora , 1992, HLT.
[75] Pfadintegrale in der Quantenphysik , 1992 .
[76] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[77] A. Fisher,et al. The Theory of critical phenomena , 1992 .
[78] Ronald R. Coifman,et al. Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.
[79] David J. C. MacKay,et al. The Evidence Framework Applied to Classification Networks , 1992, Neural Computation.
[80] Glenn Carroll,et al. Learn-ing probaballstic dependency grammars from labelled text , 1992 .
[81] W. Härdle. Applied Nonparametric Regression , 1992 .
[82] William H. Press,et al. Numerical recipes in C (2nd ed.): the art of scientific computing , 1992 .
[83] Yaser S. Abu-Mostafa,et al. A Method for Learning From Hints , 1992, NIPS.
[84] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[85] M. Bernhard. Introduction to Chaotic Dynamical Systems , 1992 .
[86] Jeffrey Mark Siskind,et al. Naive physics, event perception, lexical semantics, and language acquisition , 1992 .
[87] T. Poggio,et al. 3D Object Recognition: Symmetry and Virtual Views , 1992 .
[88] David Haussler,et al. Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..
[89] C. Beck,et al. Thermodynamics of chaotic systems , 1993 .
[90] Roberto Brunelli,et al. Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..
[91] T. Watkin,et al. THE STATISTICAL-MECHANICS OF LEARNING A RULE , 1993 .
[92] J. Besag,et al. Spatial Statistics and Bayesian Computation , 1993 .
[93] Jeffrey Mark Siskind. Lexical Acquisition as Constraint Satisfaction , 1993 .
[94] Morris Halle,et al. Distributed morphology and the pieces of inflection , 1993 .
[95] Tomaso Poggio,et al. Example Based Image Analysis and Synthesis , 1993 .
[96] David H. Wolpert,et al. Bayesian Backpropagation Over I-O Functions Rather Than Weights , 1993, NIPS.
[97] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[98] Biing-Hwang Juang,et al. Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.
[99] Yaser S. Abu-Mostafa,et al. Hints and the VC Dimension , 1993, Neural Computation.
[100] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.
[101] Jeffrey Mark Siskind,et al. Lexical Acquisition in the Presence of Noise and Homonymy , 1994, AAAI.
[102] Andrew R. Webb,et al. Functional approximation by feed-forward networks: a least-squares approach to generalization , 1994, IEEE Trans. Neural Networks.
[103] Alan L. Yuille,et al. Statistical Physics, Mixtures of Distributions, and the EM Algorithm , 1994, Neural Computation.
[104] Martin Kay,et al. Regular Models of Phonological Rule Systems , 1994, CL.
[105] C. Snow,et al. Input and interaction in language acquisition: The changing role of negative evidence in theories of language development , 1994 .
[106] Carl de Marcken. The Acquisition of a Lexicon from Paired Phoneme Sequences and Semantic Representations , 1994, ICGI.
[107] A. Cutler. Segmentation problems, rhythmic solutions * , 1994 .
[108] Umesh V. Vazirani,et al. An Introduction to Computational Learning Theory , 1994 .
[109] David Beymer,et al. Face recognition under varying pose , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.
[110] B. Silverman,et al. Nonparametric regression and generalized linear models , 1994 .
[111] L. Tierney. Markov Chains for Exploring Posterior Distributions , 1994 .
[112] Michael Kenstowicz,et al. Phonology In Generative Grammar , 1994 .
[113] J. J. Kosowsky,et al. Statistical Physics Algorithms That Converge , 1994, Neural Computation.
[114] Michael C. Burl,et al. Finding faces in cluttered scenes using random labeled graph matching , 1995, Proceedings of IEEE International Conference on Computer Vision.
[115] Carl E. Rasmussen,et al. In Advances in Neural Information Processing Systems , 2011 .
[116] Peter L. Balise,et al. Boolean Algebra and Its Applications , 1995 .
[117] Frédéric Bimbot,et al. Language modeling by variable length sequences: theoretical formulation and evaluation of multigrams , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.
[118] Rudolf Podgornik,et al. Statistical thermodynamics of surfaces, interfaces, and membranes , 1995 .
[119] Todd K. Leen,et al. From Data Distributions to Regularization in Invariant Learning , 1995, Neural Computation.
[120] Geoffrey E. Hinton,et al. The Helmholtz Machine , 1995, Neural Computation.
[121] Geoffrey E. Hinton,et al. The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.
[122] Eric Sven Ristad,et al. New Techniques for Context Modeling , 1995, ACL.
[123] Inhomogeneous Random Phase Approximation: A Solvable Model , 1995 .
[124] F. Girosi,et al. Prior knowledge and the creation of "virtual" examples for RBF networks , 1995, Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing.
[125] Shun-ichi Amari,et al. Information geometry of the EM and em algorithms for neural networks , 1995, Neural Networks.
[126] Yoshua Bengio,et al. Pattern Recognition and Neural Networks , 1995 .
[127] Hill,et al. Annealed Theories of Learning , 1995 .
[128] William T. Freeman,et al. Presented at: 2nd Annual IEEE International Conference on Image , 1995 .
[129] W. Ebeling. Stochastic Processes in Physics and Chemistry , 1995 .
[130] Hans-Paul Schwefel,et al. Evolution and optimum seeking , 1995, Sixth-generation computer technology series.
[131] Valeriu Beiu,et al. Density Estimation as a Preprocessing Step for Constructive Algorithms , 1995, SNN Symposium on Neural Networks.
[132] Physics of critical fluctuations , 1995 .
[133] Inhomogeneous Random Phase Approximation for Nuclear and Atomic Reactions , 1995 .
[134] Laurenz Wiskott,et al. Labeled graphs and dynamic link matching for face recognition and scene analysis , 1995 .
[135] Michael I. Jordan. Why the logistic function? A tutorial discussion on probabilities and neural networks , 1995 .
[136] Christopher M. Bishop,et al. Current address: Microsoft Research, , 2022 .
[137] Carl de Marcken,et al. Lexical Heads, Phrase Structure and the Induction of Grammar , 1995, VLC@ACL.
[138] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[139] Tomaso A. Poggio,et al. Regularization Theory and Neural Networks Architectures , 1995, Neural Computation.
[140] Stanley F. Chen,et al. Bayesian Grammar Induction for Language Modeling , 1995, ACL.
[141] Michael I. Jordan,et al. Mean Field Theory for Sigmoid Belief Networks , 1996, J. Artif. Intell. Res..
[142] David H. Wolpert,et al. The Existence of A Priori Distinctions Between Learning Algorithms , 1996, Neural Computation.
[143] Alexander J. Smola,et al. Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.
[144] Dimitar P. Filev,et al. Fuzzy SETS AND FUZZY LOGIC , 1996 .
[145] San Cristóbal Mateo,et al. The Lack of A Priori Distinctions Between Learning Algorithms , 1996 .
[146] Richard M. Golden,et al. Mathematical Methods for Neural Network Analysis and Design , 1996 .
[147] John Shawe-Taylor,et al. A framework for structural risk minimisation , 1996, COLT '96.
[148] Penio S. Penev,et al. Local feature analysis: A general statistical theory for object representation , 1996 .
[149] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[150] Callan,et al. Field Theories for Learning Probability Distributions. , 1996, Physical review letters.
[151] David Barber,et al. Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo , 1996, NIPS.
[152] V. Buzek,et al. From quantum Bayesian inference to quantum tomography , 1997, quant-ph/9701029.
[153] Michael I. Jordan,et al. Computing upper and lower bounds on likelihoods in intractable networks , 1996, UAI.
[154] R W Prager,et al. Development of low entropy coding in a recurrent network. , 1996, Network.
[155] A. Murat Tekalp,et al. Face detection and facial feature extraction using color, shape and symmetry-based cost functions , 1996, Proceedings of 13th International Conference on Pattern Recognition.
[156] Eytan Domany,et al. Data Clustering Using a Model Granular Magnet , 1997, Neural Computation.
[157] Joachim M. Buhmann,et al. Pairwise Data Clustering by Deterministic Annealing , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[158] Federico Girosi,et al. Support Vector Machines: Training and Applications , 1997 .
[159] Dorothea Heiss-Czedik,et al. An Introduction to Genetic Algorithms. , 1997, Artificial Life.
[160] Radford M. Neal. Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification , 1997, physics/9701026.
[161] Vijay Balasubramanian,et al. Statistical Inference, Occam's Razor, and Statistical Mechanics on the Space of Probability Distributions , 1996, Neural Computation.
[162] John D. Lafferty,et al. Inducing Features of Random Fields , 1995, IEEE Trans. Pattern Anal. Mach. Intell..
[163] Tomaso A. Poggio,et al. Pedestrian detection using wavelet templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[164] Alex Pentland,et al. Parametrized structure from motion for 3D adaptive feedback tracking of faces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[165] C. Papageorgiou,et al. Object and pattern detection in video sequences , 1997 .
[166] Vladimir Cherkassky,et al. The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.
[167] Terrence J. Sejnowski,et al. Learning Nonlinear Overcomplete Representations for Efficient Coding , 1997, NIPS.
[168] Bernhard Schölkopf,et al. On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion , 1998, Algorithmica.
[169] Tomaso A. Poggio,et al. Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[170] Tomaso A. Poggio,et al. A Sparse Representation for Function Approximation , 1998, Neural Computation.
[171] John Shawe-Taylor,et al. Structural Risk Minimization Over Data-Dependent Hierarchies , 1998, IEEE Trans. Inf. Theory.
[172] Takeo Kanade,et al. Rotation invariant neural network-based face detection , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).
[173] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[174] Takeo Kanade,et al. Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[175] Takio Kurita,et al. Scale and Rotation Invariant Recognition Method Using Higher-Order Local Autocorrelation Features of Log-Polar Image , 1998, ACCV.
[176] J. C. BurgesChristopher. A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .
[177] Jorma Rissanen,et al. Stochastic Complexity in Statistical Inquiry , 1989, World Scientific Series in Computer Science.
[178] Tomaso A. Poggio,et al. A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).
[179] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[180] Takeo Kanade,et al. Probabilistic modeling of local appearance and spatial relationships for object recognition , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).
[181] Federico Girosi,et al. An Equivalence Between Sparse Approximation and Support Vector Machines , 1998, Neural Computation.
[182] Tomaso Poggio,et al. Incorporating prior information in machine learning by creating virtual examples , 1998, Proc. IEEE.
[183] Kurt Binder,et al. Monte Carlo Simulation in Statistical Physics , 1992, Graduate Texts in Physics.
[184] K. Schittkowski,et al. NONLINEAR PROGRAMMING , 2022 .