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- 1,273
- Pythagorean fuzzy MCDM method based on CoCoSo and CRITIC with score function for 5G industry evaluation301
The main advantages of proposed algorithm are: (1) have no counterintuitive phenomena; (2) without division or antilogarithm by zero problem; (3) own stronger ability to distinguish alternatives.
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- Learning to Purification for Unsupervised Person Re-Identification100
This work proposes to take advantage of the knowledge of teacher model in an offline scheme to purify the label noise, and achieves a state-of-the-art accuracy 85.8% @mAP and 94.5% @Rank-1 on the challenging Market-1501 benchmark with ResNet-50 under the fully unsupervised setting.
- A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification79
A novel learnable dictionary encoding layer is proposed in this paper that can accumulate high-order statistics from variable-length input sequence and generate an utterance level fixed-dimensional vector representation.
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- Dependency-based long short term memory network for drug-drug interaction extraction71
A dependency-based deep neural network model that can learn effective relation information with less feature engineering in the task of DDI extraction is proposed, and the experimental results show that the model excels at balancing the Precision and Recall values.
- Dual Transfer Learning70
This paper proposes a novel approach, Dual Transfer Learning (DTL), which simultaneously learns the marginal and conditional distributions, and exploits the duality between them in a principled way.
- Automated evaluation of building alignments in generalized maps51
This article firstly analyses the requirements (constraints) related to the generalization of building alignments, then, it focuses on three more specific constraints, i.e. on existence, orientation of alignments and spatial distribution of composing buildings.
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- Maximum Mean and Covariance Discrepancy for Unsupervised Domain Adaptation49
An unsupervised learning model based on MMCD abbreviated as McDA was proposed and efficiently optimized to resolve the domain adaptation problem, and experiments on image classification show that McDA outperforms other representative domain adaptation methods, which implies the effectiveness of MMCD in domain adaptation.
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- Gene Ranking of RNA-Seq Data via Discriminant Non-Negative Matrix Factorization43
DNMF is suggested to be an effective method for the analysis of differential gene expression and gene ranking for RNA-seq data, substantially outperforming all other benchmarked methods.
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- CRD: Fast Co-clustering on Large Datasets Utilizing Sampling-Based Matrix Decomposition.40
A general framework for fast co-clustering large datasets, CRD is proposed, which achieves competitive accuracy but with much less computational cost by utilizing recently developed sampling-based matrix decomposition methods.
- CRD40
A general framework for fast co-clustering large datasets, CRD is proposed, which achieves competitive accuracy but with much less computational cost by utilizing recently developed sampling-based matrix decomposition methods.
- Characterization and detection of building patterns in cartographic data: Two algorithms38
This paper studies existing topographic maps at large to medium scales, and proposes and discusses a comprehensive typology of building patterns, their distinctions and characteristics, which includes linear alignments, curvilinear, align-along-road alignments and nonlinear clusters.
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- Supervised Mineral Prospectivity Mapping via Class-Balanced Focal Loss Function on Imbalanced Geoscience Datasets33
A novel class-balanced focal loss function (CBFL) is provided to address the training data imbalance issue in deep learning modeling for MPM and it is demonstrated that the CBFL outperformed the other loss functions.
- Universum Prescription: Regularization Using Unlabeled Data33
It is shown that simply prescribing "none of the above" labels to unlabeled data has a beneficial regularization effect to supervised learning.
- An Integrated Framework for Data-Driven Mineral Prospectivity Mapping Using Bagging-Based Positive-Unlabeled Learning and Bayesian Cost-Sensitive Logistic Regression32
An integrated framework for data-driven MPM is proposed, which is denoted as BPUL–BCSLR, with Bagging-based positive-unlabeled learning (BPUL) and Bayesian cost-sensitive logistic regression ( BCSLR) in order to address simultaneously the issues mentioned above.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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