Theoretical concerns in machine learning

Learning Pathways

computing > machine learning

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

Sébastien Bubeck, Mark Sellke

2021 ArXiv

PAPER

Training Neural Networks is ER-complete

Mikkel Abrahamsen, L. Kleist, Tillmann Miltzow et al.

2021 ArXiv

PAPER

Understanding deep learning requires rethinking generalization

Chiyuan Zhang, Samy Bengio, Moritz Hardt et al.

2016 ICLR

PAPER

The Lack of A Priori Distinctions Between Learning Algorithms

D. Wolpert

1996 Neural Computation

PAPER

Draft : Deep Learning in Neural Networks : An Overview

J. Schmidhuber

2014 Neural Networks

PAPER

On the Number of Linear Regions of Deep Neural Networks

Guido Montúfar, Razvan Pascanu, Kyunghyun Cho et al.

2014 NIPS

PAPER

An exact mapping between the Variational Renormalization Group and Deep Learning

Pankaj Mehta, D. Schwab

2018

PAPER

Deep learning via Hessian-free optimization

James Martens

2010 ICML

PAPER

Why Does Deep and Cheap Learning Work So Well?

Henry W. Lin, Max Tegmark

2016 ArXiv

PAPER

1 Efficient BackProp

Yann LeCun, L. Bottou, G. Orr et al.

2012

PAPER

Neural Networks and the Bias/Variance Dilemma

Stuart Geman, Elie Bienenstock, Rene Doursat et al.

1992 Neural Computation

PAPER

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

Y. Gal, Zoubin Ghahramani

2015 NIPS

PAPER

The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Jonathan Frankle, Michael Carbin

2018 ICLR

PAPER

What's hidden in the hidden layers?

D. Touretzky, D. Pomerleau

1989

PAPER

The Description Length of Deep Learning models

Léonard Blier, Y. Ollivier

2018 NeurIPS

PAPER

Provable Bounds for Learning Some Deep Representations

Sanjeev Arora, Aditya Bhaskara, Rong Ge et al.

2013 ICML

PAPER

Bottom-up Deep Learning using the Hebbian Principle

Aseem Wadhwa, Upamanyu Madhow

2016

PAPER

Group theoretical methods in machine learning

Risi Kondor

2008

BOOK

Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design

Yoav Levine, David Yakira, Nadav Cohen et al.

2017 ICLR

PAPER

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