How far can we go without convolution: Improving fully-connected networks

9 November 2015

Papers citing "How far can we go without convolution: Improving fully-connected networks"

16 / 16 papers shown

Title
Artificial Intelligence and Deep Learning Algorithms for Epigenetic Sequence Analysis: A Review for Epigeneticists and AI Experts Muhammad Tahir Mahboobeh Norouzi Shehroz S. Khan James Davie Soichiro Yamanaka A. Ashraf 35 1 0 01 Apr 2025
Polynomial Time Cryptanalytic Extraction of Deep Neural Networks in the Hard-Label Setting Nicholas Carlini J. Chávez-Saab Anna Hambitzer Francisco Rodríguez-Henríquez Adi Shamir AAML 30 1 0 08 Oct 2024
Towards Exact Computation of Inductive Bias Akhilan Boopathy William Yue Jaedong Hwang Abhiram Iyer Ila Fiete 44 0 0 22 Jun 2024
Emergent representations in networks trained with the Forward-Forward algorithm Niccolo Tosato Lorenzo Basile Emanuele Ballarin Giuseppe de Alteriis Alberto Cazzaniga A. Ansuini 26 9 0 26 May 2023
Model-agnostic Measure of Generalization Difficulty Akhilan Boopathy Kevin Liu Jaedong Hwang Shu Ge Asaad Mohammedsaleh Ila Fiete 80 4 0 01 May 2023
Deep Neural Networks as Complex Networks Emanuele La Malfa G. Malfa Claudio Caprioli Giuseppe Nicosia Vito Latora PINN GNN 48 4 0 12 Sep 2022
SPINE: Soft Piecewise Interpretable Neural Equations Jasdeep Singh Grover Harsh Minesh Domadia Rajashree Tapase Grishma Sharma 21 0 0 20 Nov 2021
Cascaded Classifier for Pareto-Optimal Accuracy-Cost Trade-Off Using off-the-Shelf ANNs Cecilia Latotzke Johnson Loh T. Gemmeke 24 0 0 27 Oct 2021
ResMLP: Feedforward networks for image classification with data-efficient training Hugo Touvron Piotr Bojanowski Mathilde Caron Matthieu Cord Alaaeldin El-Nouby ... Gautier Izacard Armand Joulin Gabriel Synnaeve Jakob Verbeek Hervé Jégou VLM 36 656 0 07 May 2021
Computational Separation Between Convolutional and Fully-Connected Networks Eran Malach Shai Shalev-Shwartz 24 26 0 03 Oct 2020
Variational Information Distillation for Knowledge Transfer Sungsoo Ahn S. Hu Andreas C. Damianou Neil D. Lawrence Zhenwen Dai 58 609 0 11 Apr 2019
Biologically plausible deep learning -- but how far can we go with shallow networks? Bernd Illing W. Gerstner Johanni Brea 24 94 0 27 Feb 2019
Classifying Signals on Irregular Domains via Convolutional Cluster Pooling Angelo Porrello Davide Abati Simone Calderara Rita Cucchiara 24 11 0 13 Feb 2019
Markov chain Hebbian learning algorithm with ternary synaptic units Guhyun Kim V. Kornijcuk Dohun Kim Inho Kim Jaewook Kim Hyo Cheon Woo Jihun Kim C. S. Hwang D. Jeong 27 3 0 23 Nov 2017
The loss surface of deep and wide neural networks Quynh N. Nguyen Matthias Hein ODL 51 283 0 26 Apr 2017
Do Deep Convolutional Nets Really Need to be Deep and Convolutional? G. Urban Krzysztof J. Geras Samira Ebrahimi Kahou Ozlem Aslan Shengjie Wang R. Caruana Abdel-rahman Mohamed Matthai Philipose Matthew Richardson 20 47 0 17 Mar 2016