Sparsity‐based autoencoders for denoising cluttered radar signatures by Shobha Sundar Ram, Shelly Vishwakarma, Akanksha Sneh, Kainat Yasmeen

“…Furthermore, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal‐to‐noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single‐layer DAE. …”
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Minimum Error Entropy Algorithms with Sparsity Penalty Constraints by Zongze Wu, Siyuan Peng, Wentao Ma, Badong Chen, Jose C. Principe

“…Recently, sparse adaptive learning algorithms have been developed to exploit system sparsity as well as to mitigate various noise disturbances in many applications. …”
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Better approximations for Tree Sparsity in Nearly-Linear Time by Backurs, Arturs, Indyk, Piotr, Schmidt, Ludwig

“…The Tree Sparsity problem is defined as follows: given a node-weighted tree of size n and an integer k, output a rooted subtree of size k with maximum weight. …”
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Lower Bounds on the Column Sparsity of Compressed Sensing Matrices by Nachin, Mergen

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Nonparametric High-dimensional Models: Sparsity, Efficiency, Interpretability by Ibrahim, Shibal

“…The focus of this thesis is on considering various sparsity and structural constraints within these methods and develop optimization based approaches to enhance training efficiency, inference, and/or interpretability. …”
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Efficient Deep Learning with Sparsity: Algorithms, Systems, and Applications by Liu, Zhijian

“…In this dissertation, we present our solutions across the algorithm, system, and application stacks to address the demand-supply gap through the lens of sparsity. In Part I, we first develop algorithms, SparseViT and SparseRefine, which identify sparsity within dense input data. …”
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Transform-domain sparsity regularization for inverse problems in geosciences by Jarapour, Behnam, Goyal, Vivek K., McLaughlin, Dennis, Freeman, William T.

“…Where we have tested our sparsity regulariza-tion approach, it has performed better than traditional alter-natives.…”
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Notes on PCA, Regularization, Sparsity and Support Vector Machines by Poggio, Tomaso, Girosi, Federico

“…In addition to extending the close relations between regularization, Support Vector Machines and sparsity, our work also illuminates and formalizes the LFA concept of Penev and Atick (1996). …”
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Reconciliation of statistical and spatial sparsity for robust visual classification by Cheng, Hao, Yap, Kim-Hui, Wen, Bihan

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Towards efficient large-scale learning by exploiting sparsity by Tan, Ming Kui

“…To enable efficient and effective big data analysis, this thesis exploits the sparsity constraints of learning tasks and investigates large-scale learning in three directions, namely feature selection for classification tasks, sparse recovery for signal processing, and matrix recovery problem. …”
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Exploring the sparsity of seismic wave propagation in complex media by Haindl, CM

“…I find that the sparsity of wavefields persists in the presence of complex local-scale structures such as thrust faults and salt bodies and in models with full anisotropy. …”

Deep neural network initialization with sparsity inducing activations by Price, I, Daultry Ball, N, Jones, AC, Chun Hei Lam, S, Tanner, J

On sparsity, power-law and clustering properties of graphex processes by Caron, F, Panero, F, Rousseau, J

“…Finally, we propose a class of models within this framework where one can separately control the latent structure and the global sparsity/power-law properties of the graph.…”

Chordal sparsity in control and optimization of large-scale systems by Zheng, Y

“…</p> <p>The first part of this thesis proposes a new conversion framework for large-scale SDPs characterized by chordal sparsity. This framework is analogous to standard conversion techniques for interior-point methods, but is more suitable for the application of first-order methods. …”

Incorporating users satisfaction to resolve sparsity in recommendation systems by Kamal, S., Ibrahim, R., Ghani, I., Ziauddin, A.

“…Nevertheless, there are many problems to be faced, specifically; sparseness, cold start, prediction accuracy, as well as scalability which can all result in a challenge of accuracy over the recommendation systems. A sparsity rate of 95% is experienced in CF-based commercial recommendation applications. …”

Load flow analysis by matlab using sparsity technique [microfilm] / by Chen, Ming Wah

An Affine Combination of Adaptive Filters for Channels with Different Sparsity Levels by M. Butsenko, T. Trump

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Sparsity-based method for ring artifact elimination in computed tomography. by Mona Selim, Essam A Rashed, Mohammed A Atiea, Hiroyuki Kudo

“…We propose to minimize some sparsity-induced norms corresponding to the imperfect error components to effectively eliminate the ring artifact. …”
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On necessity of L-stationarity in Nonlinear Optimization with a Sparsity Constraint by Abbas Khademi, Majid Soleimani-damaneh

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Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction by Xiaofeng Liao, Xiangjun Li, Qingyong Xu, Hu Wu, Yongji Wang

“…This mechanism helps to identify the users and the items most related, even in the case of sparsity, and can capture the drift of user preferences over time. …”
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