Fetching the paper…
Reading the bibliography…
Interpretable machine learning has gained much attention recently.
Amino acid substitution matrices from protein blocks
Henikoff, S. and Henikoff, J. G · 1992
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
The information bottleneck method
Tishby, N., Pereira, F. C., and Bialek, W · 2000
Earlier work this paper cites.
How to explain individual classification decisions
Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., and Müller, K.-R · 2010
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., and Potts, C · 2011
Earlier work this paper cites.
Elements of information theory
Cover, T. M. and Thomas, J. A · 2012
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
The immune epitope database (iedb) 3.0
Vita, R., Overton, J. A., Greenbaum, J. A., Ponomarenko, J., Clark, J. D., Cantrell, J. R., Wheeler, D. K., Gabbard, J. L., Hix, D., Sette, A., et al · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 2015
Earlier work this paper cites.
Deep learning and the information bottleneck principle
Tishby, N. and Zaslavsky, N · 2015
Earlier work this paper cites.
Layer-wise relevance propagation for neural networks with local renormalization layers
Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R., and Samek, W · 2016
Cited alongside, same era.
The mythos of model interpretability
Lipton, Z. C · 2016
Cited alongside, same era.
How many TCR clonotypes does a body maintain?
Lythe, G., Callard, R. E., Hoare, R. L., and Molina-París, C · 2016
Cited alongside, same era.
Why should i trust you?: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Cited alongside, same era.
Deep variational information bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K · 2017
Cited alongside, same era.
Real time image saliency for black box classifiers
Dabkowski, P. and Gal, Y · 2017
Right for the right reasons: Training differentiable models by constraining their explanations
Ross, A. S., Hughes, M. C., and Doshi-Velez, F · 2017
Later among the works it cites.
Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
Later among the works it cites.
Vdjdb: a curated database of t-cell receptor sequences with known antigen specificity
Shugay, M., Bagaev, D. V., Zvyagin, I. V., Vroomans, R. M., Crawford, J. C., Dolton, G., Komech, E. A., Sycheva, A. L., Koneva, A. E., Egorov, E. S., et al · 2017
Later among the works it cites.
Opening the black box of deep neural networks via information
Shwartz-Ziv, R. and Tishby, N · 2017
Later among the works it cites.
Smoothgrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
Doshi-Velez, F. and Kim, B · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Fong, R. C. and Vedaldi, A · 2017
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Cited alongside, same era.
Later among the works it cites.
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
Later among the works it cites.
Visualizing deep neural network decisions: Prediction difference analysis
Zintgraf, L. M., Cohen, T. S., Adel, T., and Welling, M · 2017
Later among the works it cites.
Learning to explain: An information-theoretic perspective on model interpretation
Chen, J., Song, L., Wainwright, M. J., and Jordan, M. I · 2018
Later among the works it cites.
Nettcr: sequence-based prediction of tcr binding to peptide-mhc complexes using convolutional neural networks
Jurtz, V. I., Jessen, L. E., Bentzen, A. K., Jespersen, M. C., Mahajan, S., Vita, R., Jensen, K. K., Marcatili, P., Hadrup, S. R., Peters, B., et al · 2018
Later among the works it cites.
Rise: Randomized input sampling for explanation of black-box models
Petsiuk, V., Das, A., and Saenko, K · 2018
Later among the works it cites.
Tcrgp: Determining epitope specificity of t cell receptors
Jokinen, E., Heinonen, M., Huuhtanen, J., Mustjoki, S., and Lähdesmäki, H · 2019
Closest in time.