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Conditional Neural Relational Inference for Interacting Systems

arXiv 2106.11083 ↗First posted 21 Jun 2021cs.LGcited by 28

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Abstract (arXiv)

In this work, we want to learn to model the dynamics of similar yet distinct groups of interacting objects. These groups follow some common physical laws that exhibit specificities that are captured through some vectorial description. We develop a model that allows us to do conditional generation from any such group given its vectorial description. Unlike previous work on learning dynamical systems that can only do trajectory completion and require a part of the trajectory dynamics to be provided as input in generation time, we do generation using only the conditioning vector with no access to generation time's trajectories. We evaluate our model in the setting of modeling human gait and, in particular pathological human gait.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 21 of its 33 references that are in the map. First posted on arXiv 21 Jun 2021.

Builds on

21 of 33 references.

References · 21

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9 more
  1. 2018Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactionscited by 100
  2. 2017Visual Interaction Networkscited by 94
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 6; 2023: 5; 2024: 6; 2025: 1; 2026: 8.

28 papers.

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  1. 2022Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvaturecited by 96
  2. 2023Everything is Connected: Graph Neural Networkscited by 85
  3. 2023Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothingcited by 67
  4. 2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
  5. 2021Discrete-Valued Neural Communicationcited by 39
  6. 2022Copula Conformal Prediction for Multi-step Time Series Forecastingcited by 28
  7. 2021Roto-translated Local Coordinate Frames For Interacting Dynamical Systemscited by 24
  8. 2024Equivariance via Minimal Frame Averaging for More Symmetries and Efficiencycited by 18
  9. 2022Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Modelscited by 12
  10. 2023Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networkscited by 10
  11. 2023Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-time Dynamicscited by 8
  12. 2024Disentangled Neural Relational Inference for Interpretable Motion Predictioncited by 7
16 more
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  4. 2022Learning to Drop Out: An Adversarial Approach to Training Sequence VAEscited by 3
  5. 2026Learning Physics-Guided Residual Dynamics for Deformable Object Simulationcited by 2
  6. 2024Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecastingcited by 1
  7. 2024F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Datacited by 1
  8. 2024Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interactioncited by 1
  9. 2025EqCollide: Equivariant and Collision-Aware Deformable Objects Neural Simulatorcited by 1
  10. 2024A Joint Spectro-Temporal Relational Thinking Based Acoustic Modeling Frameworkcited by 0
  11. 2026Learning Disease-Sensitive Latent Interaction Graphs From Noisy Cardiac Flow Measurementscited by 0
  12. 2026Subjective-Graph LLM Agents for Simulating Uncertainty in Classroom Social Perceptioncited by 0
  13. 2026BRICKS-WM: Building Reusability via Interface Composition Kinetics for Structured World Modelscited by 0
  14. 2026A foundation model of numerical intelligence with cross-disciplinary generalizationcited by 0
  15. 2026Fundamental Dynamical Units for Physics-Informed Structural Inference from Perturbation Time-Series in Networked Systemscited by 0
  16. 2026Structural Inference under Hidden Agentscited by 0