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Poster Sessions

 

Monday, December 5 -- Tuesday, December 6 --

Wednesday, December 7, 2004

 

7:30 pm – 12:00 midnight

 

The Poster Sessions, will take place on three evenings during the Conference, and will offer high-quality posters and an opportunity for researchers to share their work and exchange ideas in an collegial setting.  The majority of contributions accepted at NIPS are presented as posters.

 

Monday, December 5, 2005

 

Posters:

1.

Y. Bengio, H. Larochelle, V. Pascal: Non-Local Manifold Parzen Windows

2.

R. Lippert, R. Rifkin: Asymptotics of Gaussian Regularized Least Squares

3.

N. Loeff, H. Arora, A. Sorokin, D. Forsyth: Efficient Unsupervised Learning for Localization and Detection in Object Categories

4.

A. Lozano, S. Kulkarni, R. Schapire: Convergence and Consistency of Regularized Boosting Algorithms With Stationary B-Mixing Observations

5.

U. Maoz, E. Portugaly, T. Flash, Y. Weiss: Noise and the Two-thirds Power Law

6.

T. Murayama, P. Davis: Rate Distortion Codes in Sensor Networks

7.

J. Murillo-Fuentes, F. Perez-Cruz: Gaussian Processes for Multiuser Detection in CDMA Receivers

8.

I. Murray, D. MacKay, Z. Ghahramani, J. Skilling: Nested Sampling for Potts Models

9.

Y. Nakashita, T. Shibata: An Analog Visual Pre-Processing Processor

10.

M. Narasimhan, N. Jojic, J. Bilmes: Q-Clustering

11.

V. Navalpakkam, L. Itti: Optimal Cue Selection Strategy

12.

A. Navot, L. Shpigelman, N. Tishby, E. Vaadia: Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity

13.

D. Neill, A. Moore, G. Cooper: A Bayesian Spatial Scan Statistic

14.

X. Nguyen, M. Wainwright, M. Jordan: Divergence Measures, Surrogate Loss Functions and Experimental Design

15.

G. Nolte, A. Ziehe, F. Meinecke, K. Mueller: Analyzing Coupled Brain Sources: Distinguishing True From Spurious Interaction

16.

M. Opper: An Approximate Inference Approach for the PCA Reconstruction Error

17.

M. Oster, S. Liu: Spiking Inputs to a Winner-take-all Network

18.

J. Palmer, K. Kreutz-Delgado, D. Wipf, B. Rao: Variational EM Algorithms for Non-Gaussian Latent Variable Models

19.

L. Paninski: Nonparametric Inference of Prior Probabilities From Bayes-optimal Behavior

20.

O. Pasternak, N. Sochen, N. Intrator, Y. Assaf: Neuronal Fiber Delineation in Area of Edema From Diffusion Weighted MRI

21.

P. Sajda, J. Wielaard: Neural Mechanisms of Contrast Dependent Receptive Field Size in V1

22.

J. Pfister, W. Gerstner: Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects

23.

B. Potetz, T. Lee: Scaling Laws in Natural Scenes and the Inference of 3D Shape

24.

D. Precup, R. Sutton, C. Paduraru, S. Singh: Off-policy Learning With Recognizers

25.

X. Ren, C. Fowlkes, J. Malik: Cue Integration for Figure/Ground Labeling

26.

P. Gehler, M. Welling: Products of "Edge-perts"

27.

M. Rucci: Visual Encoding With Jittering Eyes

28.

P. Sarkar, A. Moore: Dynamic Social Network Analysis Using Latent Space Models

29.

E. Saund: Logic and MRF Circuitry for Labeling Occluding and Thinline Visual Contours

30.

A. Saxena, S. Chung, A. Ng: Learning Depth From Single Monocular Images

31.

R. Sayres, D. Ress, K. Grill-Spector: Identifying Distributed Object Representations in Human Extrastriate Visual Cortex

32.

M. Schmitt, L. Martignon: On the Accuracy of Bounded Rationality: How Far From Optimal Is Fast and Frugal?

33.

N. Schraudolph, J. Yu, D. Aberdeen: Fast Online Policy-Gradient Learning With SMD Gain Vector Adaptation

34.

B. Schumitsch, S. Thrun, G. Bradski, K. Olukotun: The Information-Form Data Association Filter

35.

O. Schwartz, T. Sejnowski, P. Dayan: A Bayesian Framework for Tilt Perception and Confidence

36.

C. Scott, R. Nowak: Learning Minimum Volume Sets

37.

R. Serrano-Gotarredona, B. Linares-Barranco, P. Lichtsteiner, A. Linares-Barranco, A. Civit, T. Serrano-Gotarredona, P. Häfliger, S. Liu, T. Delbruck, M. Oster: AER Building Blocks for Multi-Layers Multi-Chips Neuromorphic Vision Systems

38.

Y. Shen, A. Ng, M. Seeger: Fast Gaussian Process Regression Using KD-Trees

39.

A. Shon, K. Grochow, A. Hertzmann, R. Rao: Gaussian Process CCA for Image Synthesis and Robotic Imitation

40.

J. Silva: Selecting Landmark Points for Sparse Manifold Learning

41.

L. Song, E. Gysels, E. Gordon: Phase Synchrony Rates for the Recognition of Motor Imageries in BCIs

42.

S. Sra, I. Dhillon: Generalized Nonnegative Matrix Approximations With Bregman Divergences

43.

M. Steyvers, S. Brown: Prediction and Change Detection

44.

E. Sudderth, A. Torralba, W. Freeman, A. Willsky: Describing Visual Scenes Using Transformed Dirichlet Processes

45.

M. Sugiyama: Active Learning for Misspecified Models

46.

R. Sutton, E. Rafols, A. Koop: Temporal Abstraction in Temporal-difference Networks

47.

J. Suzuki, H. Isozaki: Sequence and Tree Kernels With Statistical Feature Mining

48.

B. Taba, K. Boahen: Silicon Growth Cones map Silicon Retina

49.

B. Taskar, S. Lacoste-Julien, M. Jordan: Structured Prediction via the Extragradient Method

50.

R. Thurman, W. Noble, J. Vert: Kernels for Gene Regulatory Regions

51.

J. Ting, A. D'Souza, K. Yamamoto, T. Yoshioka, D. Hoffman, L. Sergio, S. Kakei, J. Kalaska, M. Kawato, P. Strick, S. Schaal: Predicting EMG Data From M1 Neurons With Variational Bayesian Least Squares

52.

A. Van Schaik, R. Reeve, C. Jin, T. Hamilton: An AVLSI Cricket Ear Model

53.

M. Wainwright: Stable Message-passing and Convex Surrogates: Joint Parameter Estimation and Prediction

54.

M. Wakin, S. Sarvotham, M. Duarte, D. Baron, R. Baraniuk: Recovery of Jointly Sparse Signals From Few Random Projections

55.

M. Warmuth: A Quantum Bayes Rule

56.

K. Watanabe, S. Watanabe: Variational Bayesian Stochastic Complexity of Mixture Models

57.

G. Weintraub, C. Benkard, B. Van Roy: Approximations for Markov Perfect Industry Dynamics With Large Numbers of Heterogeneous Firms

58.

B. Wen, K. Boahen: Active Bidirectional Coupling in a Cochlear Chip

59.

K. Wong, D. Saad, Z. Gao: Message Passing for Task Redistribution on Sparse Graphs

60.

K. Yu, S. Yu, V. Tresp: Soft Hierarchical Clustering With Similarities

61.

G. Zelinsky, W. Zhang, B. Yu, X. Chen, D. Samaras: The Role of Top-down and Bottom-up Processes in Guiding Eye Movements During Visual Search

62.

H. Zha, Z. Zhang: A Domain Decomposition Method for Fast Manifold Learning

63.

D. Zhang, D. Gatica-Perez, S. Bengio, D. Roy: Learning Influence Among Interacting Markov Chains

64.

L. Zhang, D. Samaras, N. Alia-Klein, N. Volkow, R. Goldstein: Modeling Neuronal Interactivity Using Dynamic Bayesian Networks

65.

L. Zhu, A. Yuille: A Hierarchical Compositional System for Rapid Object Detection

 

Tuesday, December 6, 2005

 

Posters:

1.

P. Abbeel, V. Ganapathi, A. Ng: Learning Vehicular Dynamics, With Application to Modeling Helicopters

2.

D. Aberdeen: Policy-Gradient Methods for Planning

3.

F. Agakov, D. Barber: Kernelized Infomax Clustering

4.

M. Ahrens, Q. Huys, L. Paninski: Large-scale Biophysical Parameter Estimation in Single Neurons via Constrained Linear Regression

5.

Y. Altun, D. McAllester, M. Belkin: Margin Semi-Supervised Learning for Structured Variables

6.

D. Arathorn: A Cortically Plausible Inverse Problem Solving Method Applied to Recognizing Static and Kinematic 3D

7.

A. Argyriou, M. Herbster, M. Pontil: Combining Graph Laplacians for Semi--Supervised Learning

8.

D. Bagnell, A. Ng: On Local Rewards and Scaling Distributed Reinforcement Learning

9.

C. Baker, J. Tenenbaum, R. Saxe: Bayesian Models of Human Action Understanding

10.

Y. Bengio, O. Delalleau, N. Le Roux: The Curse of Highly Variable Functions for Local Kernel Machines

11.

D. Blatt, A. Hero: From Weighted Classification to Policy Search

12.

B. Bryan, J. Schneider, R. Nichol, C. Miller, C. Genovese, L. Wasserman: Active Learning For Identifying Function Threshold Boundaries

13.

R. Bunescu, R. Mooney: Subsequence Kernels for Relation Extraction

14.

R. Castro, R. Willett, R. Nowak: Faster Rates in Regression via Active Learning

15.

N. Cesa-Bianchi, C. Gentile: Improved Risk Tail Bounds for On-line Algorithms

16.

A. Chan, N. Vasconcelos: Layered Dynamic Textures

17.

Y. Chen, X. Ji: Size Regularized Cut for Data Clustering

18.

K. Crammer, M. Kearns, J. Wortman: Learning From Data of Variable Quality

19.

N. De Freitas, Y. Wang, M. Mahdaviani, D. Lang: Fast Krylov Methods for N-Body Learning

20.

O. Dekel, Y. Singer: Data-Driven Online to Batch Conversions

21.

R. Der, D. Lee: Beyond Gaussian Processes: On the Distributions of Infinite Networks

22.

J. Diebel, S. Thrun: An Application of Markov Random Fields to Range Sensing

23.

C. Do, A. Ng: Meta-learning for Text Classification

24.

E. Doi, D. Balcan, M. Lewicki: A Theoretical Analysis of Robust Coding Over Noisy Overcomplete Channels

25.

G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, K. Mueller: Optimizing Spatio-temporal Filters for Improving Brain-Computer Interfacing

26.

M. Dudik, R. Schapire, S. Phillips: Correcting Sample Selection Bias in Maximum Entropy Density Estimation

27.

A. Eliazar, P. Ronald: Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps

28.

J. Farquhar, D. Hardoon, H. Meng, J. Shawe-Taylor, S. Szedmak: Two View Learning: SVM-2K, Theory and Practice

29.

D. Fleet, J. Wang, A. Hertzmann: Gaussian Process Dynamical Models

30.

L. Liao, D. Fox, H. Kautz: Location-based Activity Recognition

31.

B. Frey, D. Dueck: Mixture Modeling by Affinity Propagation

32.

T. Griffiths, Z. Ghahramani: Infinite Latent Feature Models and the Indian Buffet Process

33.

X. He, D. Cai, P. Niyogi: Tensor Subspace Analysis

 

Posters of Spotlights:

34.

D. Blei, J. Lafferty: Correlated Topic Models

35.

N. Bruce, J. Tsotsos: Saliency Based on Information Maximization

36.

Y. Engel, P. Szabo, D. Volkinshtein: Learning to Control an Octopus Arm With Gaussian Process Temporal Difference Methods

37.

F. Fleuret, G. Blanchard: Pattern Recognition From One Example by Chopping

38.

J. Johnson, D. Malioutov, A. Willsky: Walk-Sum Interpretation and Analysis of Gaussian Belief Propagation

39.

M. Kuss, C. Rasmussen: Assessing Approximations for Gaussian Process Classification

40.

H. Lu, A. Yuille: Ideal Observers for Detecting Human Motion: Correspondence Noise

41.

S. Mahadevan, M. Maggioni: Value Function Approximation With Diffusion Wavelets and Laplacian Eigenfunctions

42.

P. McCracken, M. Bowling: Online Discovery and Learning of Predictive State Representations

43.

C. Moallemi, B. Van Roy: Consensus Propagation

44.

B. Moghaddam, Y. Weiss, S. Avidan: Spectral Bounds for Sparse PCA: Exact and Greedy Algorithms

45.

G. Orban, J. Fiser, R. Aslin, M. Lengyel: Bayesian Model Learning in Human Visual Perception

46.

C. Sminchisescu, A. Kanaujia, Z. Li, D. Metaxas: Conditional Visual Tracking in Kernel Space

47.

N. Usunier, M. Amini, P. Gallinari: Generalization Error Bounds for Classifiers Trained With Interdependent Data

48.

B. Van Roy: Performance Loss Bounds for Approximate Value Iteration With State Aggregation

49.

D. Verma, R. Rao: Goal-Based Imitation as Probabilistic Inference Over Graphical Models

50.

P. Viola, J. Platt: Multiple Instance Boosting for Object Detection

51.

D. Wipf, B. Rao: Comparing the Effects of Different Weight Distributions on Finding Sparse Representations

52.

J. Zhang, Z. Ghahramani, Y. Yang: Learning Multiple Related Tasks Using Latent Independent Component Analysis

53.

L. Zwald, G. Blanchard: On the Convergence of Eigenspaces in Kernel Principal Component Analysis

 

Posters of Talks:

54.

B. Anderson, A. Moore: Efficient Value of Information for Graphical Models

55.

S. Dasgupta: Coarse Sample Complexity Bounds for Active Learning

56.

J. Edwards, D. Forsyth: Searching for Character Models

57.

Z. Ghahramani, K. Heller: Bayesian Sets

58.

S. Kakade, A. Kalai: From Batch to Transductive Online Learning

59.

E. Krupka, N. Tishby: Generalization in Clustering With Unobserved Features

60.

M. Mozer, M. Shettel, S. Vecera: Top-Down Control of Visual Attention: A Rational Account

61.

Y. Niv, N. Daw, P. Dayan: How Fast to Work: Response Vigor, Motivation and Tonic Dopamine

62.

P. Ravikumar, J. Lafferty: Preconditioner Approximations for Probabilistic Graphical Models

63.

E. Snelson, Z. Ghahramani: Sparse Parametric Gaussian Processes

64.

R. Vert, J. Vert: Consistency of One-class SVM and Related Algorithms

65.

W. Zhang, H. Yang, D. Samaras, G. Zelinsky: A Computational Model of Eye Movements During Object Class Detection

 

Wednesday, December 7, 2005

 

Posters:

1.

Y. Bengio, N. Le Roux, V. Pascal, O. Delalleau, P. Marcotte: Convex Neural Networks

2.

G. Blanchard, M. Sugiyama, M. Kawanabe, V. Spokoiny, K. Mueller: Non-Gaussian Component Analysis: a Semiparametric Framework for Linear Dimension Reduction

3.

O. Dekel, S. Shalev-Shwartz, Y. Singer: The Forgetron: A Kernel-Based Perceptron on a Fixed Budget

4.

F. Wood, S. Roth, M. Black: Modeling Neural Population Spiking Activity With Gibbs Distributions

5.

G. Fung, R. Rosales, B. Krishnapuram: Learning Rankings via Convex Hull Separation

6.

T. Gaertner, Q. Le, S. Burton, A. Smola, S. Vishwanathan: Large-Scale Multiclass Transduction

7.

A. Garcez, L. Lamb, D. Gabbay: A Connectionist Model for Constructive Modal Reasoning

8.

T. Roos, P. Grünwald, P. Myllymäki, H. Tirri: Generalization to Unseen Cases

9.

T. Geng, B. Porr, F. Woergoetter: Fast Biped Walking With a Reflexive Controller and Real-time Policy Searching

10.

R. Gilad-Bachrach, A. Navot, N. Tishby: Query by Committee Made Real

11.

A. Globerson, S. Roweis: Metric Learning by Collapsing Classes

12.

S. Goldwater, T. Griffiths, M. Johnson: Interpolating Between Types and Tokens by Estimating Power-law Generators

13.

Y. Grandvalet, J. Mariéthoz, S. Bengio: A Probabilistic Interpretation of SVMs With an Application to Unbalanced Classification

14.

L. Gunter, J. Zhu: Computing the Solution Path for the Regularized Support Vector Regression

15.

F. Hamze, N. De Freitas: Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs

16.

X. He, D. Cai, P. Niyogi: Laplacian Score for Feature Selection

17.

T. Hertz, I. Weiner, D. Weinshall, I. Nelken: Analyzing Auditory Neurons by Learning Distance Functions

18.

G. Hinton, V. Nair: Inferring Motor Programs From Images of Handwritten Digits

19.

W. Huang, L. Jiao: Response Analysis of Neuronal Population With Synaptic Depression

20.

Y. Huang, B. Jenkins: Non-iterative Estimation With Perturbed Gaussian Markov Processes

21.

J. Hurri: Learning Cue-Invariant Visual Responses

22.

L. Itti, P. Baldi: Bayesian Surprise Attracts Human Attention

23.

H. Jaeger, M. Zhao, A. Kolling: Efficient Estimation of OOMs

24.

V. Jain, V. Zhigulin, H. Seung: Representing Part-Whole Relationships in Recurrent Neural Networks

25.

R. Jin, C. Ding, F. Kang: A Probabilistic Approach for Optimizing Spectral Clustering

26.

R. Jolivet, A. Rauch, H. Lüscher, W. Gerstner: Integrate-and-Fire Models With Adaptation are Good Enough

27.

A. Juditsky, A. Nazin, A. Tsybakov, N. Vayatis: Generalization Error Bounds for Aggregation by Mirror Descent With Averaging

28.

S. Kakade, M. Seeger, D. Foster: Worst-Case Bounds for Gaussian Process Models

29.

A. Kapoor, Y. Qi, H. Ahn, R. Picard: Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification

30.

Y. Karklin, M. Lewicki: Fully Adaptable Scale Mixture Models Learn Multiscale Codes for Natural Images

31.

S. Keerthi, W. Chu: A Matching Pursuit Approach to Sparse Gaussian Process Regression

32.

M. Keller, S. Bengio, S. Wong: Surprising Outcome While Benchmarking Statistical Tests

33.

S. Kim, A. Magnani, S. Boyd: Robust Fisher Discriminant Analysis

34.

K. Klinkner, C. Shalizi, M. Camperi: Measuring Shared Information and Coordinated Activity in Neuronal Networks

35.

O. Kreidl, A. Willsky: Inference With Minimal Communication: a Decision-Theoretic Variational Approach

36.

J. Kubica, J. Masiero, A. Moore, R. Jedicke, A. Connolly: Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery

37.

J. Lafferty, L. Wasserman: Rodeo: Sparse Nonparametric Regression in High Dimensions

38.

T. Lange, J. Buhmann: Fusion of Similarity Data in Clustering

39.

F. Laviolette, M. Marchand, M. Shah: A PAC-Bayes Approach to the Set Covering Machine

40.

D. Lee, A. Gray, A. Moore: Dual-Tree Fast Gauss Transforms

41.

R. Legenstein, W. Maass: A Criterion for the Convergence of Learning With Spike Timing Dependent Plasticity

42.

A. Levina, M. Herrmann: Dynamical Synapses Give Rise to a Power-Law Distribution of Neuronal Avalanches

43.

F. Li, Y. Yang, E. Xing: From Lasso Regression to Feature Vector Machine

44.

X. Liao, L. Carin: Radial Basis Function Network for Multi-task Learning

45.

K. Likharev, J. Lee, X. Ma: CMOL CrossNets: Possible Neuromorphic Nanoelectronic Circuits

46.

N. Masuda, S. Amari: Modeling Memory Transfer and Saving in Cerebellar Motor Learning

47.

S. McClure, M. Gilzenrat, J. Cohen: An Exploration-exploitation Model Based on Norepinepherine and Dopamine Activity

48.

E. Meeds, S. Osindero: An Alternative Infinite Mixture Of Gaussian Process Experts

49.

D. Mochihashi, Y. Matsumoto: Context as Filtering

50.

A. Yuille: Augmented Rescorla-Wagner and Maximum Likelihood Estimation

 

Posters of Spotlights:

51.

M. Aupetit: Learning Topology With the Generative Gaussian Graph and the EM Algorithm

52.

A. Celik, M. Stanacevic, G. Cauwenberghs: Gradient Flow Independent Component Analysis in Micropower VLSI

53.

M. Danoczy, R. Hahnloser: Efficient Estimation of Hidden State Dynamics From Spike Trains

54.

P. Dayan, A. Yu: Norepinephrine and Neural Interrupts

55.

K. Fukumizu, F. Bach, A. Gretton: Statistical Convergence of Kernel CCA

56.

N. Jojic, V. Jojic, B. Frey, C. Meek, D. Heckerman: Using Epitomes to Model Genetic Diversity

57.

K. Lang: Fixing two Weaknesses of the Spectral Method

58.

Y. LeCun, U. Muller, J. Ben, E. Cosatto, B. Flepp: Off-Road Obstacle Avoidance Through End-to-End Learning

59.

S. Sonnenburg, G. Raetsch, C. Schaefer: A General and Efficient Multiple Kernel Learning Algorithm

60.

M. Tamosiunaite, B. Porr, F. Woergoetter: Temporally Changing Synaptic Plasticity

61.

X. Wang, N. Mohanty, A. McCallum: Group and Topic Discovery From Relations and Text

62.

T. Zhang, R. Ando: Analysis of Spectral Kernel Design Based Semi-supervised Learning

 

 

Posters of Talks:

63.

J. Arthur, K. Boahen: Learning in Silicon: Timing is Everything

64.

P. Flaherty, M. Jordan, A. Arkin: Robust Design of Biological Experiments

65.

W. Maass, P. Joshi, E. Sontag: Principles of Real-time Computing With Feedback Applied to Cortical Microcircuit Models

66.

K. Miura, M. Okada, S. Amari: Unbiased Estimator of Shape Parameter for Spiking Irregularities Under Changing Environments

67.

B. Nadler, S. Lafon, R. Coifman, I. Kevrekidis: Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators

68.

S. Nagarajan, H. Attias, K. Hild, K. Sekihara: Stimulus Evoked Independent Factor Analysis of MEG Data With Large Background Activity

69.

M. Raginsky, S. Lazebnik: Estimation of Intrinsic Dimensionality Using High-Rate Vector Quantization

70.

A. Stocker, E. Simoncelli: Sensory Adaptation Within a Bayesian Framework for Perception

71.

S. Thrun: Affine Structure From Sound

72.

K. Weinberger, J. Blitzer, L. Saul: Distance Metric Learning for Large Margin Nearest Neighbor Classification

73.

C. Williams, J. Quinn, N. McIntosh: Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care

74.

B. Yu, A. Afshar, G. Santhanam, S. Ryu, K. Shenoy, M. Sahani: Extracting Dynamical Structure Embedded in Neural Activity

75.

M. Zinkevich, A. Greenwald, M. Littman: Cyclic Equilibria in Markov Games

76.

Y. Zhang, Z. Changshui: Separation of Music Signals by Harmonic Structure Modeling