# Reading

> Source: https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/reading/ · Updated: 2025-11-18

- [Article - Deep, deep trouble, Elad](https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/reading/article-deep-deep-trouble-elad/)
- [Paper - ADADELTA, An Adaptive Learning Rate Method](https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/reading/paper-adadelta-an-adaptive-learning-rate-method/)
- [Paper - Attention Is All You Need (2017)](https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/reading/paper-attention-is-all-you-need-2017/)
- [Paper - Error bounds for approximations with deep ReLU networks, Yarotsky (2016)](https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/reading/paper-error-bounds-for-approximations-with-deep-relu-networks-yarotsky-2016/)
- [Paper - Explaining and harnessing adversarial examples (2015)](https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/reading/paper-explaining-and-harnessing-adversarial-examples-2015/)
- [Paper - Exponential expressivity in deep neural networks through transient chaos (2016)](https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/reading/paper-exponential-expressivity-in-deep-neural-networks-through-transient-chaos-2016/)
- [Paper - Gradient-based learning applied to document recognition, LeCun](https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/reading/paper-gradient-based-learning-applied-to-document-recognition-lecun/)
- [Paper - Optimal nonlinear approximation, DeVore (1989)](https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/reading/paper-optimal-nonlinear-approximation-devore-1989/)
- [Paper - Representation Benefits of Deep Feedforward Networks, Telgarsky (2015)](https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/reading/paper-representation-benefits-of-deep-feedforward-networks-telgarsky-2015/)
- [Paper - When and when can deep networks avoid the curse of dimensionality, Poggio (2016)](https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/reading/paper-when-and-when-can-deep-networks-avoid-the-curse-of-dimensionality-poggio-2016/)

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