Results 241 to 250 of about 6,507,474 (284)

DEEP LEARNING OF SEMI-COMPETING RISK DATA VIA A NEW NEURAL EXPECTATION-MAXIMIZATION ALGORITHM. [PDF]

Ann Appl Stat
Salerno S, Zhang Z, Li Y.
europepmc   +1 more source

Using Over-the-Counter Retail Medication Sales to Detect and Track Influenza-Like Illnesses Including Novel Diseases: Surveillance Modeling Study. [PDF]

JMIR Public Health Surveill
Ye Y, Espino J, Aronis JM, Hochheiser H, Michaels MG, Cooper GF.   +5 more
europepmc   +1 more source

Estimating Chronic Kidney Disease Stage Transitions from Irregular Electronic Health Record Data Using an Expectation-Maximization Framework. [PDF]

MDM Policy Pract
Qi W, Lobo JM, Yan G, Ghenbot R, Sands KG, Krupski TL, Culp SH, Otero-León DF.   +7 more
europepmc   +1 more source

Source-Free Class-Balanced Pseudo-Label Adaptation for Large-Scale Cross-Day WiFi Transmitter Identification. [PDF]

Sensors (Basel)
Yang Y, Zhang H, Wang X, Li M, Li J.
europepmc   +1 more source

Secure Edge Offloading Strategy for IoT-Enabled Electromagnetic Sensing Systems. [PDF]

Sensors (Basel)
Xue T, Xia Y, Chen X.
europepmc   +1 more source

Modeling of Years of Cohabitation, Children Ever Born, and Number of Living Children of Women of Reproductive Age Using Trivariate Poisson Regression. [PDF]

Health Sci Rep
Tawiah K, Frempong NK, Adebanji AO, Ansah RK.   +3 more
europepmc   +1 more source

Disentangling the Effects of Counterfactual Feedback on Maximization and Risk Preference across Gains and Losses

Shiravand A, Gueguen M, Bavard S, Wulff D, Bastin J, Palminteri S.   +5 more
europepmc   +1 more source

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Continuous Influence Maximization

ACM Transactions on Knowledge Discovery from Data, 2020
Imagine we are introducing a new product through a social network, where we know for each user in the network the function of purchase probability with respect to discount. Then, what discounts should we offer to those social network users so that, under a predefined budget, the adoption of the product is maximized in expectation?
Yu Yang 0001, Xiangbo Mao, Jian Pei 0001, Xiaofei He 0001   +3 more
openaire   +1 more source

Large-scale influence maximization with the influence maximization benchmarker suite

Proceedings of the 4th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), 2021
Maximizing the influence of a fixed size seed set in a graph was investigated intensively in the past decade. Two very relevant questions are 1) how to solve the influence maximization problem on very large graphs within short time and 2) how to compare possible findings with the current state-of-the-art in a fair manner.
Heiko Geppert, Sukanya Bhowmik, Kurt Rothermel   +2 more
openaire   +2 more sources

Adaptive Influence Maximization

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019
Information diffusion and social influence are more and more present in today's Web ecosystem. Having algorithms that optimize the presence and message diffusion on social media is indeed crucial to all actors (media companies, political parties, corporations, etc.) who advertise on the Web.
Bogdan Cautis, Silviu Maniu, Nikolaos Tziortziotis   +2 more
openaire   +2 more sources