Caesar.jl
  • Introduction
  • Welcome
    • Installation
    • FAQ
  • Getting Started
    • Initial Concepts
    • Building Factor Graphs
    • Solving and Interacting
    • Internal Variables/Factors
    • Multi-Modal/Hypothesis
    • Flux (NN) Factors
    • Plotting (2D)
    • Entry=>Data Blob
    • Multi-Language Support
    • Cloud Server/Database
    • Multi-session/agent Solving
    • Visualization (3D)
  • Examples
    • Caesar Examples
    • ContinuousScalar as 1D Example
    • Under-defined Trilateration, 2D
    • Hexagonal 2D SLAM
    • Fixed-Lag Solving 2D
    • ROS Middleware
    • Dead Reckon Tether
  • Principles
    • Filters vs. Graphs
    • Generic Convolutions
    • Multiplying Functions (.py)
    • Bayes (Junction) tree
    • Advanced Bayes Tree Topics
    • Multimodal iSAM Algorithm
  • How to Expand?
    • Custom Variables and Factors
    • Creating Variables
    • Creating Factors
  • Developer Zone
    • Wiki Pointers
    • Creating DynPose Factor
    • Known Issue List
    • Internal Functions
  • Literature
    • References
  • Function Reference
    • Caesar's Reference
Version
  • Getting Started
  • Flux (NN) Factors
  • Flux (NN) Factors
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Incorporating Neural Network Factors

IncrementalInference.jl and RoME.jl has native support for using Neural Networks (via Flux.jl) as non-Gaussian factors. Documentation is forthcoming, but meanwhile see the following generic Flux.jl factor structure. Note also that a standard Mixture approach already exists too.

« Multi-Modal/HypothesisPlotting (2D) »

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