Jordan_M: 57 documents, from the least to the most probable paper
Perplexity score: Title:
16021.141529 An Orthogonally Persistent Java
14555.432409 Defining and Handling Transient Fields in PJama
12421.622291 Stable Algorithms for Link Analysis
11444.012719 Link Analysis, Eigenvectors and Stability
11390.932164 Software Configuration Management in an Object Oriented Database
10866.502027 Orthogonal Persistence for Java - A Mid-term Report
10650.204982 Perceptual Distortion Contributes to the Curvature of Human Reaching Movements
8993.696034 The Modula-3 Type System
7496.403139 Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study
5788.676642 Thin Junction Trees
5609.631154 Computational structure of coordinate transformations: A generalization study
5440.815082 Triangulation by Continuous Embedding
5347.908248 On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes
4573.713334 On Spectral Clustering: Analysis and an algorithm
4169.431543 Computational Models of Sensorimotor Integration
3986.847281 Loopy Belief Propagation for Approximate Inference: An Empirical Study
3435.187142 Reinforcement Learning with Soft State Aggregation
3290.628048 PEGASUS: A policy search method for large MDPs and POMDPs
3278.094602 Variational MCMC
3084.818537 Latent Dirichlet Allocation
3072.670740 Convergence of Stochastic Iterative Dynamic Programming Algorithms
2868.604138 Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection
2726.807805 Learning in Boltzmann Trees
2572.551722 Learning Without State-Estimation in Partially Observable Markovian Decision Processes
2567.443566 On the Convergence of Stochastic Iterative Dynamic Programming Algorithms
2509.850567 Local Linear Perceptrons for Classification
2433.742436 Mean Field Theory for Sigmoid Belief Networks
2410.739124 Mixture Representations for Inference and Learning in Boltzmann Machines
2395.655527 Hidden Markov decision trees
2345.201365 Attractor Dynamics in Feedforward Neural Networks
2237.755089 Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems
2132.637487 Exploiting Tractable Substructures in Intractable Networks
1941.764859 Triangulation by Continuous Embedding
1913.928093 Estimating Dependency Structure as a Hidden Variable
1844.297940 Learning with Mixtures of Trees
1799.948322 Convergence study and improvement of variational methods with MCMC
1778.129758 Active Learning with Statistical Models
1737.600550 Probabilistic Independence Networks for Hidden Markov Probability Models
1666.647296 A Mean Field Learning Algorithm For Unsupervised Neural Networks
1612.667018 A variational approach to Bayesian logistic regression models and their extensions
1606.168823 Estimating Dependency Structure as a Hidden Variable
1514.691649 Mean Field Theory for Sigmoid Belief Networks
1508.150634 Approximating Posterior Distributions in Belief Networks using Mixtures
1413.673294 Bayesian parameter estimation through variational methods
1375.833079 Computing upper and lower bounds on likelihoods in intractable networks
1347.279459 Bayesian parameter estimation via variational methods
1328.114339 Learning Fine Motion by Markov Mixtures of Experts
1311.496157 Improving the Mean Field Approximation via the Use of Mixture Distributions
1294.292271 Factorial Hidden Markov Models
1219.057190 Factorial Hidden Markov Models
1150.385190 Supervised learning from incomplete data via an EM approach
1148.597547 Hierarchical mixtures of experts and the EM algorithm
997.401267 Convergence results for the EM approach to mixtures of experts architectures
934.959320 Reinforcement Learning by Probability Matching
934.436091 Neural Networks
701.539912 Learning From Incomplete Data
686.826574 Factorial Hidden Markov Models