additional-resources

	A	B	C
1	Papers
2	Topics	Paper Titles	Links
3	Safety and Ethics	EU Guidelines on Ethics in Artificial Intelligence	https://www.europarl.europa.eu/RegData/etudes/BRIE/2019/640163/EPRS_BRI(2019)640163_EN.pdf
4	Dataset/Model Reporting	Datasheets for Datasets	https://arxiv.org/abs/1803.09010
5		Model Cards for Model Reporting	https://arxiv.org/abs/1810.03993
6		Troubling Trends in Machine Learning Scholarship	https://arxiv.org/abs/1807.03341
7	Attention Mechanism	Attention Is All You Need	https://arxiv.org/abs/1706.03762
8	Neural Tangent Kernels	Infinite Attention: NNGP and NTK for Deep Attention Networks	https://arxiv.org/abs/2006.10540
9	Generalization	Polylogarithmic Width Suffices for Gradient Descent to Achieve Arbitrarily Small Test Error with Shallow ReLU Networks	https://arxiv.org/abs/1909.12292
10		Backward Feature Correction: How Deep Learning Performs Deep Learning	https://arxiv.org/abs/2001.04413
11		Approximation by Superpositions of a Sigmoidal Function	https://web.njit.edu/~usman/courses/cs675_fall18/10.1.1.441.7873.pdf
12	Reinforcement Learning	Policy Gradient Methods for Reinforcement Learning with Function Approximation	https://papers.nips.cc/paper/1999/file/464d828b85b0bed98e80ade0a5c43b0f-Paper.pdf
13	Privacy	Privacy in Deep Learning: A Survey	https://arxiv.org/abs/2004.12254
14	Lottery Ticket	The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks	https://arxiv.org/abs/1803.03635
15		Proving the Lottery Ticket Hypothesis: Pruning is All You Need	https://arxiv.org/abs/2002.00585
16	Optimization	Learning Internal Representations by Error Propagation	https://apps.dtic.mil/sti/pdfs/ADA164453.pdf
17		Training a 3-Node Neural Network is NP-Complete	https://papers.nips.cc/paper/1988/file/3def184ad8f4755ff269862ea77393dd-Paper.pdf
18	Domain Adaptation and Out-of-Domain Generalization	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks	https://arxiv.org/abs/1703.03400
19	Bayesian Learning	Taking the Human Out of the Loop: A Review of Bayesian Optimization	https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7352306
20		On Calibration of Modern Neural Networks	https://arxiv.org/abs/1706.04599
21	Neural Network Compression	Distilling the Knowledge in a Neural Network	https://arxiv.org/abs/1503.02531
22	Robustness	Mitigating Unwanted Biases with Adversarial Learning	https://arxiv.org/abs/1801.07593
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26	Resources/Courses
27	Titles	Links
28	Theoretical principles for deep learning	http://mitliagkas.github.io/ift6085-dl-theory-class-2020/
29	Deep Learning's Most Important Ideas - A Brief Historical Review	https://dennybritz.com/blog/deep-learning-most-important-ideas/
30	"deep2Read" for UVA Qdata Group's Deep Learning Journal Club	https://qdata.github.io/deep2Read/
31	Neural Networks and Deep Learning	https://csc413-2020.github.io/
32	Neural Tangents: Fast and Easy Infinite Neural Networks in Python	https://openreview.net/forum?id=SklD9yrFPS
33		https://colab.research.google.com/github/google/neural-tangents/blob/master/notebooks/neural_tangents_cookbook.ipynb
34	A Paper List on Deep Learning Foundations	https://github.com/RonsenbergVI/deep-learning-foundations
35	10707 - Deep Learning	https://andrejristeski.github.io/10707-S20/syllabus.html
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