NECO 2002 - PDF Preprint Papers

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NECO
2002

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Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities

13 years 9 months ago

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In this work, we discuss practical methods for the assessment, comparison, and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model...

Aki Vehtari, Jouko Lampinen

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Id	Views	Title	Status

1	145	Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities	claim
2	127	Robust Blind Source Separation by Beta Divergence	claim
3	117	Bayesian A* Tree Search with Expected O(N) Node Expansions: Applications to Road Tracking	claim
4	106	Learning Nonregular Languages: A Comparison of Simple Recurrent Networks and LSTM	claim
5	105	Multiple Model-Based Reinforcement Learning	claim
6	104	An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models	claim
7	100	Robust Regression with Asymmetric Heavy-Tail Noise Distributions	claim
8	97	Center-Crossing Recurrent Neural Networks for the Evolution of Rhythmic Behavior	claim
9	95	Mixture of Experts Classification Using a Hierarchical Mixture Model	claim
10	94	Slow Feature Analysis: Unsupervised Learning of Invariances	claim
11	90	Associative Memory with Dynamic Synapses	claim
12	89	A Bayesian Framework for Sensory Adaptation	claim
13	88	Data-Reusing Recurrent Neural Adaptive Filters	claim
14	88	Approximation Bounds for Some Sparse Kernel Regression Algorithms	claim
15	85	Fast Curvature Matrix-Vector Products for Second-Order Gradient Descent	claim
16	80	On Different Facets of Regularization Theory	claim
17	80	Attentional Recruitment of Inter-Areal Recurrent Networks for Selective Gain Control	claim
18	80	Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure	claim
19	79	A Bayesian Approach to the Stereo Correspondence Problem	claim
20	78	Local Overfitting Control via Leverages	claim
21	77	Attractor Reliability Reveals Deterministic Structure in Neuronal Spike Trains	claim
22	75	Biophysiologically Plausible Implementations of the Maximum Operation	claim
23	74	An Amplitude Equation Approach to Contextual Effects in Visual Cortex	claim
24	74	Factorial Hidden Markov Models and the Generalized Backfitting Algorithm	claim
25	71	Learning to Recognize Three-Dimensional Objects	claim
26	70	Orthogonal Series Density Estimation and the Kernel Eigenvalue Problem	claim
27	68	Methods for Binary Multidimensional Scaling	claim
28	67	Selectively Grouping Neurons in Recurrent Networks of Lateral Inhibition	claim
29	66	Invariant Object Recognition in the Visual System with Novel Views of 3D Objects	claim
30	65	Optimal Short-Term Population Coding: When Fisher Information Fails	claim
31	65	Computational Capacity of an Odorant Discriminator: The Linear Separability of Curves	claim
32	65	Hebbian Imprinting and Retrieval in Oscillatory Neural Networks	claim
33	65	Adaptive Spatiotemporal Receptive Field Estimation in the Visual Pathway	claim
34	63	The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis	claim
35	62	Long-Term Reward Prediction in TD Models of the Dopamine System	claim
36	62	Dynamics of the Firing Probability of Noisy Integrate-and-Fire Neurons	claim
37	62	Unitary Events in Multiple Single-Neuron Spiking Activity: II. Nonstationary Data	claim
38	62	Cosine Tuning Minimizes Motor Errors	claim
39	61	An Image Analysis Algorithm for Dendritic Spines	claim
40	60	Statistical Significance of Coincident Spikes: Count-Based Versus Rate-Based Statistics	claim
41	60	Training Products of Experts by Minimizing Contrastive Divergence	claim
42	60	Scalable Hybrid Computation with Spikes	claim
43	58	A Simple Model of Long-Term Spike Train Regularization	claim
44	58	Stable Propagation of Activity Pulses in Populations of Spiking Neurons	claim
45	58	Neural Networks with Local Receptive Fields and Superlinear VC Dimension	claim
46	58	Analysis of Oscillations in a Reciprocally Inhibitory Network with Synaptic Depression	claim
47	57	Mechanisms Shaping Fast Excitatory Postsynaptic Currents in the Central Nervous System	claim
48	56	A Population Study of Integrate-and-Fire-or-Burst Neurons	claim
49	56	Integrate-and-Fire Neurons Driven by Correlated Stochastic Input	claim
50	55	Energy-Efficient Coding with Discrete Stochastic Events	claim
51	54	Independent Components of Magnetoencephalography: Localization	claim
52	54	Population Coding and Decoding in a Neural Field: A Computational Study	claim
53	53	Unitary Events in Multiple Single-Neuron Spiking Activity: I. Detection and Significance	claim
54	53	Information-Geometric Measure for Neural Spikes	claim
55	49	Measuring Information Spatial Densities	claim
56	49	What Geometric Visual Hallucinations Tell Us about the Visual Cortex	claim
57	46	Self-Organization in the Basal Ganglia with Modulation of Reinforcement Signals	claim
58	45	Universal Approximation of Multiple Nonlinear Operators by Neural Networks	claim
59	43	A Note on the Decomposition Methods for Support Vector Regression	claim

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