Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem

19 October 2021

Papers citing "Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem"

20 / 20 papers shown

Title
On the Approximation Power of Two-Layer Networks of Random ReLUs Daniel J. Hsu Clayton Sanford Rocco A. Servedio Emmanouil-Vasileios Vlatakis-Gkaragkounis 43 25 0 03 Feb 2021
Depth-Width Trade-offs for Neural Networks via Topological Entropy Kaifeng Bu Yaobo Zhang Qingxian Luo 44 8 0 15 Oct 2020
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth Guy Bresler Dheeraj M. Nagaraj 38 21 0 07 Jun 2020
Language Models are Few-Shot Learners Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan ... Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever Dario Amodei BDL 721 41,894 0 28 May 2020
Better Depth-Width Trade-offs for Neural Networks through the lens of Dynamical Systems Vaggos Chatziafratis Sai Ganesh Nagarajan Ioannis Panageas 42 15 0 02 Mar 2020
Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem Vaggos Chatziafratis Sai Ganesh Nagarajan Ioannis Panageas Tianlin Li 39 21 0 09 Dec 2019
Deep ReLU Networks Have Surprisingly Few Activation Patterns Boris Hanin David Rolnick 78 226 0 03 Jun 2019
On the Expressive Power of Deep Polynomial Neural Networks Joe Kileel Matthew Trager Joan Bruna 59 82 0 29 May 2019
Depth Separations in Neural Networks: What is Actually Being Separated? Itay Safran Ronen Eldan Ohad Shamir MDE 51 36 0 15 Apr 2019
Is Deeper Better only when Shallow is Good? Eran Malach Shai Shalev-Shwartz 48 45 0 08 Mar 2019
Depth Separation for Neural Networks Amit Daniely MDE 37 74 0 27 Feb 2017
On the ability of neural nets to express distributions Holden Lee Rong Ge Tengyu Ma Andrej Risteski Sanjeev Arora BDL 64 84 0 22 Feb 2017
Understanding Deep Neural Networks with Rectified Linear Units R. Arora A. Basu Poorya Mianjy Anirbit Mukherjee PINN 143 641 0 04 Nov 2016
Exponential expressivity in deep neural networks through transient chaos Ben Poole Subhaneil Lahiri M. Raghu Jascha Narain Sohl-Dickstein Surya Ganguli 88 591 0 16 Jun 2016
On the Expressive Power of Deep Neural Networks M. Raghu Ben Poole Jon M. Kleinberg Surya Ganguli Jascha Narain Sohl-Dickstein 61 788 0 16 Jun 2016
Benefits of depth in neural networks Matus Telgarsky 348 608 0 14 Feb 2016
The Power of Depth for Feedforward Neural Networks Ronen Eldan Ohad Shamir 213 732 0 12 Dec 2015
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 193,814 0 10 Dec 2015
Representation Benefits of Deep Feedforward Networks Matus Telgarsky 76 242 0 27 Sep 2015
On the Number of Linear Regions of Deep Neural Networks Guido Montúfar Razvan Pascanu Kyunghyun Cho Yoshua Bengio 88 1,254 0 08 Feb 2014