Assessment of model performance on sparse datasets with different degrees of sparsity (1–10 of 11 features) and sparsity distributions (Real-world, Random, Terminal, Central). Colored lines denote mean root mean squared error; shaded bands represent 99% confidence intervals.</p
Sparsity plays a key role in machine learning for several reasons including interpretability. Interp...
To restrict ourselves to the regime of sparse solutions has become the new paradigm for modern stati...
Thèse présentée a ̀ la faculte ́ des sciences et techniques de l’ingénieur pour l’obtention du g...
Assessment of model performance on sparse datasets with different degrees of sparsity (1–8 of 9 feat...
Models assessed on sparse datasets with sample size sequentially increasing twofold. Amount of spars...
Model performance assessment given different sparsity distributions. Quantity of missing data varied...
All tested sparsity distributions applied to sample dataset of 500 audiograms. Quantity of sparsity ...
The performance of the proposed model at two extreme thresholds with different sparsity.</p
The real-world data nowadays is usually in high dimension. For example, one data image can be repres...
Thesis (Ph.D.)--University of Washington, 2019The concept of `sparsity' is common to see in many top...
This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsit...
Performance of sparse and non-sparse discriminant models in internal validation compared on the same...
<p>a) Performance on absolute (red) and relative (black) abundances as a function of sample size for...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/119115/1/insr12167.pd
Despite its generic title, this thesis is about a specific notion of sparsity, the one introduced by...
Sparsity plays a key role in machine learning for several reasons including interpretability. Interp...
To restrict ourselves to the regime of sparse solutions has become the new paradigm for modern stati...
Thèse présentée a ̀ la faculte ́ des sciences et techniques de l’ingénieur pour l’obtention du g...
Assessment of model performance on sparse datasets with different degrees of sparsity (1–8 of 9 feat...
Models assessed on sparse datasets with sample size sequentially increasing twofold. Amount of spars...
Model performance assessment given different sparsity distributions. Quantity of missing data varied...
All tested sparsity distributions applied to sample dataset of 500 audiograms. Quantity of sparsity ...
The performance of the proposed model at two extreme thresholds with different sparsity.</p
The real-world data nowadays is usually in high dimension. For example, one data image can be repres...
Thesis (Ph.D.)--University of Washington, 2019The concept of `sparsity' is common to see in many top...
This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsit...
Performance of sparse and non-sparse discriminant models in internal validation compared on the same...
<p>a) Performance on absolute (red) and relative (black) abundances as a function of sample size for...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/119115/1/insr12167.pd
Despite its generic title, this thesis is about a specific notion of sparsity, the one introduced by...
Sparsity plays a key role in machine learning for several reasons including interpretability. Interp...
To restrict ourselves to the regime of sparse solutions has become the new paradigm for modern stati...
Thèse présentée a ̀ la faculte ́ des sciences et techniques de l’ingénieur pour l’obtention du g...