Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet

17 February 2018

Papers citing "Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet"

9 / 9 papers shown

Title
Efficient shallow learning as an alternative to deep learning Yuval Meir Ofek Tevet Yarden Tzach Shiri Hodassman Ronit D. Gross Ido Kanter 27 17 0 15 Nov 2022
Testing Feedforward Neural Networks Training Programs Houssem Ben Braiek Foutse Khomh AAML 11 14 0 01 Apr 2022
Training Thinner and Deeper Neural Networks: Jumpstart Regularization Carles Roger Riera Molina Camilo Rey Thiago Serra Eloi Puertas O. Pujol 27 4 0 30 Jan 2022
Full-attention based Neural Architecture Search using Context Auto-regression Yuan Zhou Haiyang Wang Shuwei Huo Boyu Wang 27 3 0 13 Nov 2021
Minority Reports Defense: Defending Against Adversarial Patches Michael McCoyd Won Park Steven Chen Neil Shah Ryan Roggenkemper Minjune Hwang J. Liu David A. Wagner AAML 8 54 0 28 Apr 2020
HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression R. Lin Ching-Yun Ko Zhuolun He Cong Chen Yuan-Chia Cheng Hao Yu G. Chesi Ngai Wong 36 6 0 28 Feb 2020
Modern CNNs for IoT Based Farms P. Gikunda 25 5 0 15 Jul 2019
MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification A. Wong M. Shafiee Michael St. Jules 12 59 0 28 Mar 2018
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 266 7,634 0 03 Jul 2012