Fetching the paper…
Reading the bibliography…
While deep learning has enabled significant advances in many areas of science, its black-box nature hinders architecture design for future artificial intelligence applications and interpretation for high-stakes decision makings.
(MIT Press), Vol. 8, (1995)
S Hihi, Y Bengio, Hierarchical recurrent neural networks for long-term dependencies in Advances in Neural Information Processing Systems · 1995
Earlier work this paper cites.
\JournalTitle
Y LeCun, The MNIST database of handwritten digits · 1998
Earlier work this paper cites.
(Springer) Vol. 2, (2009)
T Hastie, R Tibshirani, JH Friedman, The elements of statistical learning: data mining, inference, and prediction · 2009
Earlier work this paper cites.
\JournalTitle
Y Bengio, Learning deep architectures for AI · 2009
Earlier work this paper cites.
A Krizhevsky, Master’s thesis (University of Toronto) (2009)
2009
Earlier work this paper cites.
(PMLR), Vol. 9, pp. 249–256 (2010)
X Glorot, Y Bengio, Understanding the difficulty of training deep feedforward neural networks in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics · 2010
Earlier work this paper cites.
Vol. 25, (2012)
A Krizhevsky, I Sutskever, GE Hinton, ImageNet classification with deep convolutional neural networks in Advances in Neural Information Processing Systems · 2012
Earlier work this paper cites.
(Routledge), (2012)
JP Stevens, Applied multivariate statistics for the social sciences · 2012
Earlier work this paper cites.
pp. 1631–1642 (2013)
R Socher, et al., Recursive deep models for semantic compositionality over a sentiment treebank in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · 2013
Earlier work this paper cites.
pp. 818–833 (2014)
MD Zeiler, R Fergus, Visualizing and understanding convolutional networks in European Conference on Computer Vision · 2014
Earlier work this paper cites.
\JournalTitle
Y LeCun, Y Bengio, G Hinton, Deep learning · 2015
Earlier work this paper cites.
\JournalTitle
J Schmidhuber, Deep learning in neural networks: An overview · 2015
Earlier work this paper cites.
pp. 5353–5360 (2015)
K He, J Sun, Convolutional neural networks at constrained time cost in 2015 IEEE Conference on Computer Vision and Pattern Recognition · 2015
Earlier work this paper cites.
Vol. 28, (2015)
RK Srivastava, K Greff, J Schmidhuber, Training very deep networks in Advances in Neural Information Processing Systems · 2015
Earlier work this paper cites.
(PMLR), Vol. 37, pp. 448–456 (2015)
S Ioffe, C Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift in Proceedings of the 32nd International Conference on Machine Learning · 2015
Cited alongside, same era.
\JournalTitle
D Silver, et al., Mastering the game of go with deep neural networks and tree search · 2016
Cited alongside, same era.
pp. 770–778 (2016)
K He, X Zhang, S Ren, J Sun, Deep residual learning for image recognition in 2016 IEEE Conference on Computer Vision and Pattern Recognition · 2016
Cited alongside, same era.
H Xiao, K Rasul, R Vollgraf, Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms · 2017
Cited alongside, same era.
pp. 4700–4708 (2017)
G Huang, Z Liu, L Van Der Maaten, KQ Weinberger, Densely connected convolutional networks in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition · 2017
Cited alongside, same era.
\JournalTitle
\JournalTitle
M Belkin, D Hsu, S Ma, S Mandal, Reconciling modern machine-learning practice and the classical bias–variance trade-off · 2019
Later among the works it cites.
\JournalTitle
PL Bartlett, PM Long, G Lugosi, A Tsigler, Benign overfitting in linear regression · 2020
Later among the works it cites.
\JournalTitle
V Papyan, X Han, DL Donoho, Prevalence of neural collapse during the terminal phase of deep learning training · 2020
Later among the works it cites.
\JournalTitle
V Papyan, Traces of class/cross-class structure pervade deep learning spectra · 2020
Later among the works it cites.
pp. 38–45 (2020)
T Wolf, et al., Transformers: State-of-the-art natural language processing in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · 2020
Later among the works it cites.
\JournalTitle
C Fang, H He, Q Long, WJ Su, Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D Yarotsky, Error bounds for approximations with deep ReLU networks · 2017
Cited alongside, same era.
AG Howard, et al., Mobilenets: Efficient convolutional neural networks for mobile vision applications · 2017
Cited alongside, same era.
\JournalTitle
M Hutson, Has artificial intelligence become alchemy? · 2018
Cited alongside, same era.
(PMLR), pp. 3276–3285 (2018)
Y Lu, A Zhong, Q Li, B Dong, Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations in International Conference on Machine Learning · 2018
Cited alongside, same era.
\JournalTitle
A Jacot, F Gabriel, C Hongler, Neural tangent kernel: Convergence and generalization in neural networks · 2018
Cited alongside, same era.
(PMLR), Vol. 97, pp. 6105–6114 (2019)
M Tan, Q Le, EfficientNet: Rethinking model scaling for convolutional neural networks in Proceedings of the 36th International Conference on Machine Learning · 2019
Cited alongside, same era.
pp. 4593–4601 (2019)
I Tenney, D Das, E Pavlick, Bert rediscovers the classical nlp pipeline in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · 2019
Cited alongside, same era.
\JournalTitle
S Bubeck, M Sellke, A universal law of robustness via isoperimetry · 2021
Later among the works it cites.
WJ Su, Neurashed: A phenomenological model for imitating deep learning training · 2021
Later among the works it cites.
\JournalTitle
A Fawzi, et al., Discovering faster matrix multiplication algorithms with reinforcement learning · 2022
Closest in time.
T Galanti, On the implicit bias towards minimal depth of deep neural networks · 2022
Closest in time.
(PMLR), pp. 37–47 (2022)
I Ben-Shaul, S Dekel, Nearest class-center simplification through intermediate layers in Topological, Algebraic and Geometric Learning Workshops 2022 · 2022
Closest in time.
\JournalTitle
C Zhang, S Bengio, Y Singer, Are all layers created equal? · 2022
Closest in time.
\JournalTitle
VW Liang, Y Zhang, Y Kwon, S Yeung, JY Zou, Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning · 2022
Closest in time.