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4Rates of Convergence for Sparse Variational Gaussian Process Regression
D. Burt, C. Rasmussen, and M. Van Der Wilk. Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, page 862--871. Long Beach, California, USA, PMLR, (09--15 Jun 2019)
4 years ago by @kirk86
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convergence
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convergencegaussian-procesesreadingssparsityvariational
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1Convergence Rates of Variational Inference in Sparse Deep Learning
B. Chérief-Abdellatif. (2019)cite arxiv:1908.04847.
4 years ago by @kirk86
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convergence
deep-learning
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convergencedeep-learninginferencereadingssparsityvariational
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3Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers
J. Liu, Z. Xu, R. Shi, R. Cheung, and H. So. (2020)cite arxiv:2005.06870Comment: ICLR 2020, camera ready version.
5 years ago by @kirk86
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deep-learning
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1Learning Sparse Nonparametric DAGs
X. Zheng, C. Dan, B. Aragam, P. Ravikumar, and E. Xing. (2019)cite arxiv:1909.13189Comment: To appear in AISTATS 2020.
5 years ago by @kirk86
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causal-analysis
graphs
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2Parametric Programming Approach for Powerful Lasso Selective Inference without Conditioning on Signs
V. Duy, and I. Takeuchi. (2020)cite arxiv:2004.09749.
5 years ago by @kirk86
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inference
programming
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2SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics
K. Kaheman, J. Kutz, and S. Brunton. (2020)cite arxiv:2004.02322Comment: 25 pages, 9 figures, 5 tables.
5 years ago by @kirk86
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dynamic
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2Robust And Interpretable Blind Image Denoising Via Bias-Free Convolutional Neural Networks
S. Mohan, Z. Kadkhodaie, E. Simoncelli, and C. Fernandez-Granda. International Conference on Learning Representations, (2020)
5 years ago by @kirk86
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iclr2020
interpretability
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iclr2020interpretabilitynoisesparsity
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2Deep Network Classification by Scattering and Homotopy Dictionary Learning
J. Zarka, L. Thiry, T. Angles, and S. Mallat. International Conference on Learning Representations, (2020)
5 years ago by @kirk86
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dictionary
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2Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators
R. Heckel, and M. Soltanolkotabi. International Conference on Learning Representations, (2020)
5 years ago by @kirk86
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2Understanding l4-based Dictionary Learning: Interpretation, Stability, and Robustness
Y. Zhai, H. Mehta, Z. Zhou, and Y. Ma. International Conference on Learning Representations, (2020)
5 years ago by @kirk86
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3Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers
J. LIU, Z. XU, R. SHI, R. Cheung, and H. So. International Conference on Learning Representations, (2020)
5 years ago by @kirk86
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1The geometry of off-the-grid compressed sensing
C. Poon, N. Keriven, and G. Peyré. (2018)cite arxiv:1802.08464.
5 years ago by @kirk86
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3Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
Z. Liu, S. Gomes, A. Tiwari, and J. Scarlett. (2020)cite arxiv:2002.01697.
5 years ago by @kirk86
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3Lipschitz constant estimation of Neural Networks via sparse polynomial optimization
F. Latorre, P. Rolland, and V. Cevher. International Conference on Learning Representations, (2020)
5 years ago by @kirk86
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iclr2020
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1Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks
J. Yi, J. Lee, S. Hwang, and E. Yang. International Conference on Learning Representations, (2020)
5 years ago by @kirk86
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bias
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2High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models
M. Janzamin, and A. Anandkumar. Journal of Machine Learning Research, (2014)
5 years ago by @kirk86
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4The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
J. Frankle, and M. Carbin. (2018)cite arxiv:1803.03635Comment: ICLR camera ready.
5 years ago by @kirk86
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1Model Consistency for Learning with Mirror-Stratifiable Regularizers
J. Fadili, G. Garrigos, J. Malick, and G. Peyré. Proceedings of Machine Learning Research, volume 89 of Proceedings of Machine Learning Research, page 1236--1244. PMLR, (16--18 Apr 2019)
5 years ago by @kirk86
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4The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
J. Frankle, and M. Carbin. (2018)cite arxiv:1803.03635Comment: ICLR camera ready.
5 years ago by @kirk86
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deep-learning
generalization
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1Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation
zengfeng Huang, Z. Huang, Y. WANG, and K. Yi. (2019)
5 years ago by @kirk86
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neurips2019
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1Test of Time Award *0.2cm Online Dictionary Learning for Sparse Coding

publications (hide)62
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4Rates of Convergence for Sparse Variational Gaussian Process Regression

1Convergence Rates of Variational Inference in Sparse Deep Learning

3Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers

1Learning Sparse Nonparametric DAGs

2Parametric Programming Approach for Powerful Lasso Selective Inference without Conditioning on Signs

2SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics

2Robust And Interpretable Blind Image Denoising Via Bias-Free Convolutional Neural Networks

2Deep Network Classification by Scattering and Homotopy Dictionary Learning

2Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators

2Understanding l4-based Dictionary Learning: Interpretation, Stability, and Robustness

3Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers

1The geometry of off-the-grid compressed sensing

3Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors

3Lipschitz constant estimation of Neural Networks via sparse polynomial optimization

1Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks

2High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models

4The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

1Model Consistency for Learning with Mirror-Stratifiable Regularizers

4The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

1Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation

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