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Xia T, Chen X. Category-learning attention mechanism for short text filtering. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.08.076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Zhang J, Li M, Liu W, Lauria S, Liu X. Many-objective optimization meets recommendation systems: A food recommendation scenario. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.06.081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Alsaadi FE, Wang Z, Alharbi NS, Liu Y, Alotaibi ND. A new framework for collaborative filtering with p-moment-based similarity measure: Algorithm, optimization and application. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Zou J, Zhang Y, Liu H, Ma L. Monogenic features based single sample face recognition by kernel sparse representation on multiple Riemannian manifolds. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.06.113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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CF-DAML: Distributed automated machine learning based on collaborative filtering. APPL INTELL 2022. [DOI: 10.1007/s10489-021-03049-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Cheng H, Wang Z, Ma L, Liu X, Wei Z. Multi-task Pruning via Filter Index Sharing: A Many-Objective Optimization Approach. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09894-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractState-of-the-art deep neural network plays an increasingly important role in artificial intelligence, while the huge number of parameters in networks brings high memory cost and computational complexity. To solve this problem, filter pruning is widely used for neural network compression and acceleration. However, existing algorithms focus mainly on pruning single model, and few results are available to multi-task pruning that is capable of pruning multi-model and promoting the learning performance. By utilizing the filter sharing technique, this paper aimed to establish a multi-task pruning framework for simultaneously pruning and merging filters in multi-task networks. An optimization problem of selecting the important filters is solved by developing a many-objective optimization algorithm where three criteria are adopted as objectives for the many-objective optimization problem. With the purpose of keeping the network structure, an index matrix is introduced to regulate the information sharing during multi-task training. The proposed multi-task pruning algorithm is quite flexible that can be performed with either adaptive or pre-specified pruning rates. Extensive experiments are performed to verify the applicability and superiority of the proposed method on both single-task and multi-task pruning.
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A PSO-based deep learning approach to classifying patients from emergency departments. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-021-01285-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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