Fetching the paper…
Reading the bibliography…
Transformers have become a default architecture in computer vision, but understanding what drives their predictions remains a challenging problem.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · 1901
Earlier work this paper cites.
Jain, S. and Wallace, B. C. (2019) · 1902
Earlier work this paper cites.
What does BERT look at? an analysis of BERT’s attention
Clark, K., Khandelwal, U., Levy, O., and Manning, C. D. (2019) · 1906
Earlier work this paper cites.
A value for n-person games
Shapley, L. S. (1953) · 1953
Earlier work this paper cites.
Extremal principle solutions of games in characteristic function form: core, Chebychev and Shapley value generalizations
Charnes, A., Golany, B., Keane, M., and Rousseau, J. (1988) · 1988
Earlier work this paper cites.
The family of least square values for transferable utility games
Ruiz, L. M., Valenciano, F., and Zarzuelo, J. M. (1998) · 1998
Earlier work this paper cites.
Convex Optimization
Boyd, S., Boyd, S. P., and Vandenberghe, L. (2004) · 2004
Earlier work this paper cites.
Polynomial calculation of the Shapley value based on sampling
Castro, J., Gómez, D., and Tejada, J. (2009) · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
Earlier work this paper cites.
Captum: A unified and generic model interpretability library for PyTorch
Kokhlikyan, N., Miglani, V., Martin, M., Wang, E., Alsallakh, B., Reynolds, J., Melnikov, A., Kliushkina, N., Araya, C., Yan, S., et al. (2020) · 2009
Earlier work this paper cites.
An efficient explanation of individual classifications using game theory
Štrumbelj, E. and Kononenko, I. (2010) · 2010
Earlier work this paper cites.
Cats and dogs
Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C. (2012) · 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) · 2013
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R. (2014) · 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) · 2015
Earlier work this paper cites.
U-Net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
Earlier work this paper cites.
"Why should I trust you?" Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016) · 2016
Earlier work this paper cites.
Not just a black box: Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., Shcherbina, A., and Kundaje, A. (2016) · 2016
Earlier work this paper cites.
Interpretable explanations of black boxes by meaningful perturbation
Fong, R. C. and Vedaldi, A. (2017) · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I. (2017) · 2017
Earlier work this paper cites.
Feature visualization
Olah, C., Mordvintsev, A., and Schubert, L. (2017) · 2017
Earlier work this paper cites.
MURA: Large dataset for abnormality detection in musculoskeletal radiographs
Rajpurkar, P., Irvin, J., Bagul, A., Ding, D., Duan, T., Mehta, H., Yang, B., Zhu, K., Laird, D., Ball, R. L., et al. (2017) · 2017
Earlier work this paper cites.
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) · 2017
Earlier work this paper cites.
SmoothGrad: Removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M. (2017) · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q. (2017) · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017) · 2017
Cited alongside, same era.
Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B. (2018) · 2018
Cited alongside, same era.
Towards better understanding of gradient-based attribution methods for deep neural networks
Ancona, M., Ceolini, E., Öztireli, C., and Gross, M. (2018) · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
A primer in BERTology: What we know about how BERT works
Rogers, A., Kovaleva, O., and Rumshisky, A. (2020) · 2020
Later among the works it cites.
A projected stochastic gradient algorithm for estimating Shapley value applied in attribute importance
Simon, G. and Vincent, T. (2020) · 2020
Later among the works it cites.
BERTology meets biology: Interpreting attention in protein language models
Vig, J., Madani, A., Varshney, L. R., Xiong, C., Rajani, N., et al. (2020) · 2020
Later among the works it cites.
Transformer-based acoustic modeling for hybrid speech recognition
Wang, Y., Mohamed, A., Le, D., Liu, C., Xiao, A., Mahadeokar, J., Huang, H., Tjandra, A., Zhang, X., Zhang, F., et al. (2020) · 2020
Later among the works it cites.
Attribution in scale and space
Xu, S., Venugopalan, S., and Sundararajan, M. (2020) · 2020
Later among the works it cites.
Do feature attribution methods correctly attribute features?
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018) · 2018
Cited alongside, same era.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., et al. (2018) · 2018
Cited alongside, same era.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F. (2018) · 2018
Cited alongside, same era.
RISE: Randomized input sampling for explanation of black-box models
Petsiuk, V., Das, A., and Saenko, K. (2018) · 2018
Cited alongside, same era.
Data Shapley: Equitable valuation of data for machine learning
Ghorbani, A. and Zou, J. (2019) · 2019
Cited alongside, same era.
A benchmark for interpretability methods in deep neural networks
Hooker, S., Erhan, D., Kindermans, P.-J., and Kim, B. (2019) · 2019
Cited alongside, same era.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C. (2019) · 2019
Cited alongside, same era.
Zhou, Y., Booth, S., Ribeiro, M. T., and Shah, J. (2022) · 2020
Later among the works it cites.
Evaluating and aggregating feature-based model explanations
Bhatt, U., Weller, A., and Moura, J. M. (2021) · 2021
Later among the works it cites.
Transformer interpretability beyond attention visualization
Chefer, H., Gur, S., and Wolf, L. (2021) · 2021
Later among the works it cites.
Improving KernelSHAP: Practical Shapley value estimation using linear regression
Covert, I. and Lee, S.-I. (2021) · 2021
Later among the works it cites.
Explaining by removing: A unified framework for model explanation
Covert, I., Lundberg, S., and Lee, S.-I. (2021) · 2021
Later among the works it cites.
Attention flows are Shapley value explanations
Ethayarajh, K. and Jurafsky, D. (2021) · 2021
Later among the works it cites.
PyTorch library for CAM methods
Gildenblat, J. and contributors (2021) · 2021
Later among the works it cites.
Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R. (2021) · 2021
Later among the works it cites.
Missingness bias in model debugging
Jain, S., Salman, H., Wong, E., Zhang, P., Vineet, V., Vemprala, S., and Madry, A. (2021) · 2021
Later among the works it cites.
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · 2021
Later among the works it cites.
Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021) · 2021
Later among the works it cites.
Intriguing properties of vision transformers
Naseer, M. M., Ranasinghe, K., Khan, S. H., Hayat, M., Shahbaz Khan, F., and Yang, M.-H. (2021) · 2021
Later among the works it cites.
Benchmarking saliency methods for chest x-ray interpretation
Saporta, A., Gui, X., Agrawal, A., Pareek, A., Truong, S. Q., Nguyen, C. D., Ngo, V.-D., Seekins, J., Blankenberg, F. G., Ng, A. Y., et al. (2021) · 2021
Later among the works it cites.
Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and Jégou, H. (2021) · 2021
Later among the works it cites.
Seaborn: statistical data visualization
Waskom, M. L. (2021) · 2021
Later among the works it cites.
Better plain ViT baselines for ImageNet-1k
Beyer, L., Zhai, X., and Kolesnikov, A. (2022) · 2022
Closest in time.
Algorithms to estimate shapley value feature attributions
Chen, H., Covert, I. C., Lundberg, S. M., and Lee, S.-I. (2022) · 2022
Closest in time.
Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al. (2022) · 2022
Closest in time.