Latent representation models for user sequential mobility and social influence propagation by Feng, Shanshan

“…However, due to the data sparsity, it is hard to model the sequential information by conventional methods. …”
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SAFIN(FRIE)++ : type-I online mandami fuzzy inference system with application in option trading by Vo Duy Tung

“…However, unlike stock data that is updated every tick, option data is often sparse in nature. To handle the sparsity in data set, a fuzzy neural network system requires interpolation and extrapolation feature. …”
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SISTEM REKOMENDASI BERBASIS KOMBINASI SEMANTIC SIMILARITY DAN COLLABORATIVE FILTERING (STUDI KASUS PADA TOKO ACCESORIES HANDPHONE BESSELLING CELL) by , IMAM FAHRURROZI, , Dr. Azhari SN, M.T.

“…Based on the performance tests, the results conclude that the combination can reduce some weaknesses on the original collaborative filtering, especially on the cold-start item and sparsity issue. For this case, based on the results of some experiments, the best value of the semantic combination parameter is 0,30. …”

Ranking and synchronization from pairwise measurements via SVD by d'Aspremont, A, Cucuringu, M, Tyagi, H

“…We provide a detailed theoretical analysis in terms of robustness against both sampling sparsity and noise perturbations with outliers, using results from matrix perturbation and random matrix theory. …”

Sleep down state-active ID2/Nkx2.1 interneurons in the neocortex by Valero, M, Viney, TJ, Machold, R, Mederos, S, Zutshi, I, Schuman, B, Senzai, Y, Rudy, B, Buzsáki, G

“…Optogenetic activation of ID2+Nkx2.1+ interneurons in the posterior parietal cortex during NREM sleep, but not during waking, interfered with consolidation of cue discrimination memory. Despite their sparsity, DSA neurons perform critical physiological functions.…”

Discussion of: Treelets--An adaptive multi-scale basis for sparse unordered data by Meinshausen, N, Bühlmann, P

“…(iii) Interpretability of the result hinges on the sparsity of the final basis. Do we expect that the selected groups of variables will always be sufficiently small to be amenable for interpretation?…”

Decomposed structured subsets for semidefinite and sum-of-squares optimization by Miller, J, Zheng, Y, Sznaier, M, Papachristodoulou, A

“…Meanwhile, any underlying sparsity or symmetry structure may be leveraged to form an equivalent SDP with smaller positive semidefinite constraints. …”

Stability in matching markets with complex constraints by Nguyen, H, Nguyen, T, Teytelboym, A

“…We show that the degree of the constraint violation under our algorithm is proportional to the sparsity of the constraint matrix. The algorithm, therefore, provides practical constraint violation bounds for applications in contexts, such as refugee resettlement, day care allocation, and college admissions with diversity requirements. …”

Neural network verification using polynomial optimisation by Newton, M, Papachristodoulou, A

“…This method can be extended to more activation functions, and combined with recent sparsity-exploiting methods can result in a computationally acceptable method for verifying neural networks.…”

Sparse graphs using exchangeable random measures by Caron, F, Fox, E

“…We explore using completely random measures (CRMs) to define the exchangeable random measure, and we show how our CRM construction enables us to achieve sparse graphs while maintaining the attractive properties of exchangeability. We relate the sparsity of the graph to the Lévy measure defining the CRM. …”

Descriptor Learning Using Convex Optimisation by Simonyan, K, Vedaldi, A, Zisserman, A

“…We make a number of novel contributions towards this goal: first, it is shown that learning the pooling regions for the descriptor can be formulated as a convex optimisation problem selecting the regions using sparsity; second, it is shown that dimensionality reduction can also be formulated as a convex optimisation problem, using the nuclear norm to reduce dimensionality. …”

Disentangling disentanglement in variational autoencoders by Mathieu, E, Rainforth, T, Siddharth, N, Teh, Y

“…Decomposition permits disentanglement, i.e. explicit independence between latents, as a special case, but also allows for a much richer class of properties to be imposed on the learnt representation, such as sparsity, clustering, independent subspaces, or even intricate hierarchical dependency relationships. …”

An ensemble variational filter for sequential inverse problems by Farmer, C

“…Attention is given to the necessity of using a small number of densities in the mixture, the requirement to maintain sparsity of any matrices and the need to compute first and second derivatives of the misfit between predictions and measurements. …”

Efficient Bayesian Inference for Multivariate Probit Models With Sparse Inverse Correlation Matrices by Talhouk, A, Doucet, A, Murphy, K

“…Conditional independence is imposed by setting some off-diagonal elements of the inverse correlation matrix to zero and this sparsity structure is modeled using a decomposable graphical model. …”

Learning local feature descriptors using convex optimisation by Simonyan, K, Vedaldi, A, Zisserman, A

“…First, it is shown that learning the pooling regions for the descriptor can be formulated as a convex optimisation problem selecting the regions using sparsity. Second, it is shown that descriptor dimensionality reduction can also be formulated as a convex optimisation problem, using Mahalanobis matrix nuclear norm regularisation. …”

Automatic linearity detection by Birkisson, A, Driscoll, T

“…Linearity detection is closely connected to sparsity detection of Hessians, so for large-scale applications, memory savings can be made if linearity information is known. …”

A novel temporal trust-based recommender system by Moghaddam, Morteza Ghorbani, Elahian, Anousheh

“…While Collaborative Filtering (CF) recommender systems, which focus on previous indicate preferences, are known for their traditional problems such as cold-start, sparsity and modest accuracy, trust-based CF has been previously proposed to solve such issues by focusing on trust values among the users. …”
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Stationarity regions for ultrawideband channels by Zahedi, Y., Ngah, R., Mokayef, M., Zahedi, K.

“…The measured channel exhibits the sparsity behavior. Results show that the obtained SRs are between 0.27-0.32 m, where short distances show the variability of the channel. …”

Search space reduction approach for self-adaptive web service discovery in dynamic mobile environment by Garba, Salisu, Mohamad, Radziah, Saadon, Nor Azizah

“…The existing research on MWS discovery mostly focused on applying coarse-grained search space reduction that fails to deal with cold-start and data sparsity challenges at the expense of large computing resources. …”

A holistic approach to trust and reputation management in big data by Leonit Zeynalvand

“…Particularly, issues concerning the subjectivity, density, and sparsity of the evidence space do not necessarily co-exist in one Big Data environment. …”
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