Learning Pathways
Theoretical concerns in machine learning
19 items.
Why does machine learning work so well, and what are the theoretical constraints on what it can learn?
A Universal Law of Robustness via Isoperimetry
2021ArXiv
PAPER
Training Neural Networks is ER-complete
2021ArXiv
PAPER
Understanding deep learning requires rethinking generalization
2016ICLR
PAPER
The Lack of A Priori Distinctions Between Learning Algorithms
1996Neural Computation
PAPER
Draft : Deep Learning in Neural Networks : An Overview
2014Neural Networks
PAPER
On the Number of Linear Regions of Deep Neural Networks
2014NIPS
PAPER
An exact mapping between the Variational Renormalization Group and Deep Learning
2018
PAPER
Deep learning via Hessian-free optimization
2010ICML
PAPER
Why Does Deep and Cheap Learning Work So Well?
2016ArXiv
PAPER
1 Efficient BackProp
2012
PAPER
Neural Networks and the Bias/Variance Dilemma
1992Neural Computation
PAPER
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
2015NIPS
PAPER
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
2018ICLR
PAPER
What's hidden in the hidden layers?
1989
PAPER
The Description Length of Deep Learning models
2018NeurIPS
PAPER
Provable Bounds for Learning Some Deep Representations
2013ICML
PAPER
Bottom-up Deep Learning using the Hebbian Principle
2016
PAPER
Group theoretical methods in machine learning
2008
BOOK
Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design
2017ICLR
PAPER
