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What does a neural network encode about a concept as we traverse through the layers? Interpretability in machine learning is undoubtedly important, but the calculations of neural networks are very challenging to understand.
Recognizing neoplastic skin lesions: A photo guide, 1998
Rose, L · 1998
Earlier work this paper cites.
Random forests
Breiman, L · 2001
Earlier work this paper cites.
Sun attribute database: Discovering, annotating, and recognizing scene attributes
Patterson, G. and Hays, J · 2012
Earlier work this paper cites.
Using the 7-point checklist as a diagnostic aid for pigmented skin lesions in general practice: a diagnostic validation study
Walter, F. M., Prevost, A. T., Vasconcelos, J., Hall, P. N., Burrows, N. P., Morris, H. C., Kinmonth, A. L., and Emery, J. D · 2013
Earlier work this paper cites.
A feasible method for optimization with orthogonality constraints
Wen, Z. and Yin, W · 2013
Earlier work this paper cites.
Multiple object recognition with visual attention
Ba, J., Mnih, V., and Kavukcuoglu, K · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
Earlier work this paper cites.
Recurrent models of visual attention
Mnih, V., Heess, N., Graves, A., et al · 2014
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
Earlier work this paper cites.
Object detectors emerge in deep scene cnns
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2014
Earlier work this paper cites.
Natural neural networks
Desjardins, G., Simonyan, K., Pascanu, R., et al · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Earlier work this paper cites.
Attention for fine-grained categorization
Sermanet, P., Frome, A., and Real, E · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Earlier work this paper cites.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P · 2016
Earlier work this paper cites.
Reducing overfitting in deep networks by decorrelating representations
Cogswell, M., Ahmed, F., Girshick, R., Zitnick, L., and Batra, D · 2016
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Generalized backpropagation, étude de cas: Orthogonality
Harandi, M. and Fernando, B · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Full-capacity unitary recurrent neural networks
Wisdom, S., Powers, T., Hershey, J., Le Roux, J., and Atlas, L · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
Cited alongside, same era.
Densely connected convolutional networks
Ole: Orthogonal low-rank embedding-a plug and play geometric loss for deep learning
Lezama, J., Qiu, Q., Musé, P., and Sapiro, G · 2018
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Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions
Li, O., Liu, H., Chen, C., and Rudin, C · 2018
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Whitening and coloring batch transform for gans
Siarohin, A., Sangineto, E., and Sebe, N · 2018
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Educe: Explaining model decisions through unsupervised concepts extraction
Bouchacourt, D. and Denoyer, L · 2019
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This looks like that: deep learning for interpretable image recognition
Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., and Su, J. K · 2019
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Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Cited alongside, same era.
Aognets: Compositional grammatical architectures for deep learning
Li, X., Song, X., and Wu, T · 2017
Cited alongside, same era.
Learning deep architectures via generalized whitened neural networks
Luo, P · 2017
Cited alongside, same era.
Efficient orthogonal parametrisation of recurrent neural networks using householder reflections
Mhammedi, Z., Hellicar, A., Rahman, A., and Bailey, J · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M · 2017
Cited alongside, same era.
On orthogonality and learning recurrent networks with long term dependencies
Vorontsov, E., Trabelsi, C., Kadoury, S., and Pal, C · 2017
Cited alongside, same era.
Elsayed, G., Kornblith, S., and Le, Q. V · 2019
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All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously
Fisher, A., Rudin, C., and Dominici, F · 2019
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Towards automatic concept-based explanations
Ghorbani, A., Wexler, J., Zou, J. Y., and Kim, B · 2019
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The convolutional tsetlin machine
Granmo, O.-C., Glimsdal, S., Jiao, L., Goodwin, M., Omlin, C. W., and Berge, G. T · 2019
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Iterative normalization: Beyond standardization towards efficient whitening
Huang, L., Zhou, Y., Zhu, F., Liu, L., and Shao, L · 2019
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Cheap orthogonal constraints in neural networks: A simple parametrization of the orthogonal and unitary group
Lezcano-Casado, M. and Martínez-Rubio, D · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C · 2019
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Classification-by-components: Probabilistic modeling of reasoning over a set of components
Saralajew, S., Holdijk, L., Rees, M., Asan, E., and Villmann, T · 2019
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Towards interpretable object detection by unfolding latent structures
Wu, T. and Song, X · 2019
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On concept-based explanations in deep neural networks
Yeh, C.-K., Kim, B., Arik, S. O., Li, C.-L., Ravikumar, P., and Pfister, T · 2019
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digital imaging in skin lesion diagnosis, 2020
ISIC · 2020
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