ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2109.06663
  4. Cited By
An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier
  Features For Neuro-Symbolic Relational Learning

An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational Learning

11 September 2021
Jinyung Hong
Theodore P. Pavlic
ArXivPDFHTML

Papers citing "An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational Learning"

9 / 9 papers shown
Title
Neuro-Symbolic AI in 2024: A Systematic Review
Neuro-Symbolic AI in 2024: A Systematic Review
Brandon C. Colelough
William Regli
NAI
98
10
0
09 Jan 2025
Compensating Supervision Incompleteness with Prior Knowledge in Semantic
  Image Interpretation
Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretation
Ivan Donadello
Luciano Serafini
28
25
0
01 Oct 2019
Optuna: A Next-generation Hyperparameter Optimization Framework
Optuna: A Next-generation Hyperparameter Optimization Framework
Takuya Akiba
Shotaro Sano
Toshihiko Yanase
Takeru Ohta
Masanori Koyama
346
5,714
0
25 Jul 2019
A simple neural network module for relational reasoning
A simple neural network module for relational reasoning
Adam Santoro
David Raposo
David Barrett
Mateusz Malinowski
Razvan Pascanu
Peter W. Battaglia
Timothy Lillicrap
GNN
NAI
110
1,610
0
05 Jun 2017
Logic Tensor Networks for Semantic Image Interpretation
Logic Tensor Networks for Semantic Image Interpretation
Ivan Donadello
Luciano Serafini
Artur Garcez
71
210
0
24 May 2017
On the Error of Random Fourier Features
On the Error of Random Fourier Features
Danica J. Sutherland
J. Schneider
62
189
0
09 Jun 2015
Fast R-CNN
Fast R-CNN
Ross B. Girshick
ObjD
277
24,976
0
30 Apr 2015
A Review of Relational Machine Learning for Knowledge Graphs
A Review of Relational Machine Learning for Knowledge Graphs
Maximilian Nickel
Kevin Patrick Murphy
Volker Tresp
E. Gabrilovich
119
1,568
0
02 Mar 2015
Detect What You Can: Detecting and Representing Objects using Holistic
  Models and Body Parts
Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts
Xianjie Chen
Roozbeh Mottaghi
Xiaobai Liu
Sanja Fidler
R. Urtasun
Alan Yuille
72
639
0
08 Jun 2014
1