메뉴 건너뛰기




Volumn , Issue , 2016, Pages 1-7

Explaining predictions of non-linear classifiers in nlp

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL LINGUISTICS; NATURAL LANGUAGE PROCESSING SYSTEMS; NEURAL NETWORKS; SENSITIVITY ANALYSIS;

EID: 85121212264     PISSN: 0736587X     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (92)

References (19)
  • 1
    • 84940560152 scopus 로고    scopus 로고
    • On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
    • S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek. 2015. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation. PLoS ONE, 10(7):e0130140.
    • (2015) PLoS ONE , vol.10 , Issue.7 , pp. e0130140
    • Bach, S.1    Binder, A.2    Montavon, G.3    Klauschen, F.4    Müller, K.-R.5    Samek, W.6
  • 2
    • 0142166851 scopus 로고    scopus 로고
    • A Neural Probabilistic Language Model
    • Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin. 2003. A Neural Probabilistic Language Model. JMLR, 3:1137-1155.
    • (2003) JMLR , vol.3 , pp. 1137-1155
    • Bengio, Y.1    Ducharme, R.2    Vincent, P.3    Jauvin, C.4
  • 5
    • 0002206019 scopus 로고
    • Use of some sensitivity criteria for choosing networks with good generalization ability
    • Y. Dimopoulos, P. Bourret, and S. Lek. 1995. Use of some sensitivity criteria for choosing networks with good generalization ability. Neural Processing Letters, 2(6):1-4.
    • (1995) Neural Processing Letters , vol.2 , Issue.6 , pp. 1-4
    • Dimopoulos, Y.1    Bourret, P.2    Lek, S.3
  • 7
    • 0037442845 scopus 로고    scopus 로고
    • Review and comparison of methods to study the contribution of variables in artificial neural network models
    • M. Gevrey, I. Dimopoulos, and S. Lek. 2003. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160(3):249-264.
    • (2003) Ecological Modelling , vol.160 , Issue.3 , pp. 249-264
    • Gevrey, M.1    Dimopoulos, I.2    Lek, S.3
  • 8
    • 84961376850 scopus 로고    scopus 로고
    • Convolutional Neural Networks for Sentence Classification
    • Y. Kim. 2014. Convolutional Neural Networks for Sentence Classification. In Proc. of EMNLP, pages 1746-1751.
    • (2014) Proc. of EMNLP , pp. 1746-1751
    • Kim, Y.1
  • 9
    • 0032708870 scopus 로고    scopus 로고
    • Extracting decision trees from trained neural networks
    • R. Krishnan, G. Sivakumar, and P. Bhattacharya. 1999. Extracting decision trees from trained neural networks. Pattern Recognition, 32(12):1999-2009.
    • (1999) Pattern Recognition , vol.32 , Issue.12 , pp. 1999-2009
    • Krishnan, R.1    Sivakumar, G.2    Bhattacharya, P.3
  • 12
    • 85072607163 scopus 로고    scopus 로고
    • The Layer-wise Relevance Propagation Toolbox for Artificial Neural Networks
    • in press
    • S. Lapuschkin, A. Binder, G. Montavon, K.-R. Müller, and W. Samek. 2016b. The Layer-wise Relevance Propagation Toolbox for Artificial Neural Networks. JMLR. in press.
    • (2016) JMLR
    • Lapuschkin, S.1    Binder, A.2    Montavon, G.3    Müller, K.-R.4    Samek, W.5
  • 18
    • 85083953896 scopus 로고    scopus 로고
    • Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
    • K. Simonyan, A. Vedaldi, and A. Zisserman. 2014. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. In Workshop Proc. ICLR.
    • (2014) Workshop Proc. ICLR.
    • Simonyan, K.1    Vedaldi, A.2    Zisserman, A.3
  • 19
    • 84906489074 scopus 로고    scopus 로고
    • Visualizing and Understanding Convolutional Networks
    • M. D. Zeiler and R. Fergus. 2014. Visualizing and Understanding Convolutional Networks. In ECCV, pages 818-833.
    • (2014) ECCV , pp. 818-833
    • Zeiler, M. D.1    Fergus, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.