tag :: kernels | BibSonomy

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1Image Processing Operator Worksheets
https://homepages.inf.ed.ac.uk/rbf/HIPR2/wksheets.htm
3 years ago by @gameovercite
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CV
ML
computer-vision
edge-detection
kernels
machine-learning
morphology
CVMLcomputer-visionedge-detectionkernelsmachine-learningmorphology
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1Comparing Probabilities 2
Comparing Probabilities 2 UAI 2017
4 years ago by @kirk86
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kernels
probability
slides
uai2017
uncertainty
workshop
kernelsprobabilityslidesuai2017uncertaintyworkshop
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2Comparing Probabilities 1
Comparing Probabilities 1 UAI 2017
4 years ago by @kirk86
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kernels
probability
uai2017
uncertainty
workshop
kernelsprobabilityuai2017uncertaintyworkshop
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1Goodness of Fit for Latent Variable Models
The goodness of Fit for Latent Variable Models ICML 2019 Workshop
4 years ago by @kirk86
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generative-models
icml2019
kernels
probability
slides
stats
uncertainty
workshop
generative-modelsicml2019kernelsprobabilityslidesstatsuncertaintyworkshop
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1GANs with Integrable Probability Metric
GANs with Integrable Probability Metric
4 years ago by @kirk86
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kernels
probability
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kernelsprobabilitystatsuncertainty
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1Kernel Test for Goodness of Fit
Kernel Test for Goodness of Fit
4 years ago by @kirk86
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kernels
stats
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kernelsstatsuncertainty
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1Interpretable Two Sample Test
Interpretable Two Sample Test
4 years ago by @kirk86
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kernels
neurips2019
sampling
stats
uncertainty
kernelsneurips2019samplingstatsuncertainty
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1Two Sample Test
Two Sample Test NeurIPS 2019
4 years ago by @kirk86
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kernels
neurips2019
uncertainty
kernelsneurips2019uncertainty
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1Measures on Probability Distributions
Measures on Probability Distributions NeurIPS 2019
4 years ago by @kirk86
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kernels
neurips2019
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kernelsneurips2019probabilitystatsuncertainty
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1Kernel Statistical Test for Independence
Kernel Statistical Test for Independence
4 years ago by @kirk86
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kernels
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kernelssamplinguncertainty
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1A Case for Kernels
C. Patel. INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT, 7 (3): 689-693 (June 2023)
9 months ago by @ijtsrd
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Kernels
Kernels
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3Regularized multi--task learning
T. Evgeniou, and M. Pontil. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, page 109--117. (2004)
2 years ago by @becker
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multitask
multi
task
learning
subgroup
discovery
subgroups
svm
regularization
kernels
kernel
citedby:scholar:count:1674
citedby:scholar:timestamp:2021-12-15
multitaskmultitasklearningsubgroupdiscoverysubgroupssvmregularizationkernelskernelcitedby:scholar:count:1674citedby:scholar:timestamp:2021-12-15
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1Margin Maximizing Discriminant Analysis
A. Kocsor, K. Kornél, and {. Szepesvári. ECML/PKDD-2004, page 1-- 2. (2004)
4 years ago by @csaba
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classification,
extraction,
feature
kernels
learning,
supervised
classification,extraction,featurekernelslearning,supervised
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1Characterizing the Representer Theorem
Y. Yu, H. Chen, D. Schuurmans, and {. Szepesvári. ICML, page 570--578. (June 2013)
4 years ago by @csaba
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RKHS,
kernels
representer
theorem,
theory,
RKHS,kernelsrepresentertheorem,theory,
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1Learning deep kernels for exponential family densities
L. Wenliang, D. Sutherland, H. Strathmann, and A. Gretton. (2018)cite arxiv:1811.08357.
4 years ago by @kirk86
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bayesian
kernels
probability
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stats
bayesiankernelsprobabilityreadingsstats
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2Neural tangent kernels, transportation mappings, and universal approximation
Z. Ji, M. Telgarsky, and R. Xian. International Conference on Learning Representations, (2020)
4 years ago by @kirk86
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approximate
iclr2020
kernels
readings
approximateiclr2020kernelsreadings
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2A Fine-Grained Spectral Perspective on Neural Networks
G. Yang, and H. Salman. (2019)cite arxiv:1907.10599Comment: 12 pages of main text, 14 figures, 39 pages including appendix.
4 years ago by @kirk86
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deep-learning
gaussian-proceses
kernels
deep-learninggaussian-proceseskernels
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1Covariate Shift by Kernel Mean Matching
A. Gretton, A. Smola, J. Huang, M. Schmittfull, K. Borgwardt, and B. Scholkopf. (2009)
4 years ago by @kirk86
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kernels
uncertainty
kernelsuncertainty
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2Consistency of the group Lasso and multiple kernel learning
F. Bach. (2007)cite arxiv:0707.3390.
4 years ago by @kirk86
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kernels
svms
kernelssvms
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3Gaussian Process Behaviour in Wide Deep Neural Networks
A. Matthews, M. Rowland, J. Hron, R. Turner, and Z. Ghahramani. (2018)cite arxiv:1804.11271Comment: This work substantially extends the work of Matthews et al. (2018) published at the International Conference on Learning Representations (ICLR) 2018.
4 years ago by @kirk86
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bayesian
deep-learning
gaussian-proceses
kernels
readings
bayesiandeep-learninggaussian-proceseskernelsreadings
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2Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes
R. Novak, L. Xiao, J. Lee, Y. Bahri, G. Yang, J. Hron, D. Abolafia, J. Pennington, and J. Sohl-Dickstein. (2018)cite arxiv:1810.05148Comment: Published as a conference paper at ICLR 2019.
4 years ago by @kirk86
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bayesian
deep-learning
gaussian-proceses
kernels
readings
bayesiandeep-learninggaussian-proceseskernelsreadings
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2Selecting causal brain features with a single conditional independence test per feature
A. Mastakouri, B. Schölkopf, and D. Janzing. (2019)
4 years ago by @kirk86
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kernels
neurips2019
stats
kernelsneurips2019stats
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3Kernel Stein Tests for Multiple Model Comparison
J. Lim, M. Yamada, B. Schölkopf, and W. Jitkrittum. (2019)
4 years ago by @kirk86
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kernels
neurips2019
stats
kernelsneurips2019stats
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4On Exact Computation with an Infinitely Wide Neural Net
S. Arora, S. Du, W. Hu, Z. Li, R. Salakhutdinov, and R. Wang. (2019)
4 years ago by @kirk86
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deep-learning
kernels
neurips2019
deep-learningkernelsneurips2019
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1Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
S. Du, K. Hou, R. Salakhutdinov, B. Poczos, R. Wang, and K. Xu. (2019)
4 years ago by @kirk86
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kernels
neurips2019
probability
kernelsneurips2019probability
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3Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
K. Liang, G. Wang, Y. Li, R. Henao, and L. Carin. (2019)
4 years ago by @kirk86
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kernels
neurips2019
timeseries
kernelsneurips2019timeseries
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3The Functional Neural Process
C. Louizos, X. Shi, K. Schutte, and M. Welling. (2019)
4 years ago by @kirk86
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bayesian
kernels
neurips2019
stats
bayesiankernelsneurips2019stats
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2Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
C. Wei, J. Lee, Q. Liu, and T. Ma. (2019)
4 years ago by @kirk86
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generalization
kernels
neurips2019
optimization
regularisation
generalizationkernelsneurips2019optimizationregularisation
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3Distributionally Robust Optimization and Generalization in Kernel Methods
M. Staib, and S. Jegelka. (2019)
4 years ago by @kirk86
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generalization
kernels
neurips2019
optimization
robustness
generalizationkernelsneurips2019optimizationrobustness
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3Maximum Mean Discrepancy Gradient Flow
M. Arbel, A. Korba, A. Salim, and A. Gretton. (2019)cite arxiv:1906.04370.
5 years ago by @kirk86
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kernels
probability
readings
kernelsprobabilityreadings
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BibSonomy

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1Image Processing Operator Worksheets

1Comparing Probabilities 2

2Comparing Probabilities 1

1Goodness of Fit for Latent Variable Models

1GANs with Integrable Probability Metric

1Kernel Test for Goodness of Fit

1Interpretable Two Sample Test

1Two Sample Test

1Measures on Probability Distributions

1Kernel Statistical Test for Independence

publications (hide)148
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1A Case for Kernels

3Regularized multi--task learning

1Margin Maximizing Discriminant Analysis

1Characterizing the Representer Theorem

1Learning deep kernels for exponential family densities

2Neural tangent kernels, transportation mappings, and universal approximation

2A Fine-Grained Spectral Perspective on Neural Networks

1Covariate Shift by Kernel Mean Matching

2Consistency of the group Lasso and multiple kernel learning

3Gaussian Process Behaviour in Wide Deep Neural Networks

2Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes

2Selecting causal brain features with a single conditional independence test per feature

3Kernel Stein Tests for Multiple Model Comparison

4On Exact Computation with an Infinitely Wide Neural Net

1Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels

3Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods

3The Functional Neural Process

2Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel

3Distributionally Robust Optimization and Generalization in Kernel Methods

3Maximum Mean Discrepancy Gradient Flow

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