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. 1807.09244
  4. Cited By
Unsupervised Learning of Latent Physical Properties Using
  Perception-Prediction Networks

Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks

24 July 2018
David Zheng
V. Luo
Jiajun Wu
J. Tenenbaum
    SSL
    DRL
    AI4CE
ArXivPDFHTML

Papers citing "Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks"

11 / 11 papers shown
Title
Learning Attentive Neural Processes for Planning with Pushing Actions
Learning Attentive Neural Processes for Planning with Pushing Actions
Atharv Jain
Seiji Shaw
Nicholas Roy
219
0
0
24 Apr 2025
Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems
Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems
Alejandro Castañeda Garcia
Jan van Gemert
Daan Brinks
Nergis Tömen
43
0
0
02 Oct 2024
Fast Model-based Policy Search for Universal Policy Networks
Fast Model-based Policy Search for Universal Policy Networks
B. L. Semage
Thommen George Karimpanal
Santu Rana
Svetha Venkatesh
34
1
0
11 Feb 2022
Hindsight for Foresight: Unsupervised Structured Dynamics Models from
  Physical Interaction
Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction
Iman Nematollahi
Oier Mees
Lukás Hermann
Wolfram Burgard
17
15
0
02 Aug 2020
Integration of Neural Network-Based Symbolic Regression in Deep Learning
  for Scientific Discovery
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
Samuel Kim
Peter Y. Lu
Srijon Mukherjee
M. Gilbert
Li Jing
V. Ceperic
Marin Soljacic
17
162
0
10 Dec 2019
Extracting Interpretable Physical Parameters from Spatiotemporal Systems
  using Unsupervised Learning
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning
Peter Y. Lu
Samuel Kim
Marin Soljacic
AI4CE
22
59
0
13 Jul 2019
DensePhysNet: Learning Dense Physical Object Representations via
  Multi-step Dynamic Interactions
DensePhysNet: Learning Dense Physical Object Representations via Multi-step Dynamic Interactions
Zhenjia Xu
Jiajun Wu
Andy Zeng
J. Tenenbaum
Shuran Song
AI4CE
15
111
0
10 Jun 2019
Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation
  from Video
Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video
Miguel Jaques
Michael G. Burke
Timothy M. Hospedales
VGen
PINN
21
45
0
27 May 2019
Physical Primitive Decomposition
Physical Primitive Decomposition
Zhijian Liu
William T. Freeman
J. Tenenbaum
Jiajun Wu
OCL
30
28
0
13 Sep 2018
A Compositional Object-Based Approach to Learning Physical Dynamics
A Compositional Object-Based Approach to Learning Physical Dynamics
Michael Chang
T. Ullman
Antonio Torralba
J. Tenenbaum
AI4CE
OCL
250
439
0
01 Dec 2016
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and Physics
Peter W. Battaglia
Razvan Pascanu
Matthew Lai
Danilo Jimenez Rezende
Koray Kavukcuoglu
AI4CE
OCL
PINN
GNN
283
1,401
0
01 Dec 2016
1