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. |
|
|
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. |
|
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, |
|
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, |
|
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, |
|
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. |
|
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 |
|
6. |
T. Gaertner, Q.
Le, |
|
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, |
|
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. |
|
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, |
|
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 |