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agosztolai authored Jun 24, 2024
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Expand Up @@ -100,13 +100,13 @@ model = MARBLE.net(data)
model.fit(data)
```

By default, MARBLE operates in geometry-aware mode. You can enable the geometry-agnostic mode by changing the initialisation step to
By default, MARBLE operates in embedding-aware mode. You can enable the embedding-agnostic mode by changing the initialisation step to

```
model = MARBLE.net(data, params = {'inner_product_features': True})
```

Read more about the geometry-aware and geometry-agnostic modes [here](#innerproduct)
Read more about the embedding-aware and embedding-agnostic modes [here](#innerproduct)

After you have trained your model, you can evaluate your model on your dataset or another dataset to obtain an embedding of all manifold points in joint latent space (3-dimensional by default) based on their local vector field features.

Expand Down Expand Up @@ -225,16 +225,16 @@ model = MARBLE.net(data, loadpath=loadpath)
where loadpath can be either a path to the model (with a specific timestamp, or a directory to load the latest model automatically. By running `MARBLE.fit()`, training will resume from the last checkpoint.

<a name="innerproduct"></a>
### Geometry-aware and geometry-agnostic modes
### Embedding-aware and embedding-agnostic modes

One of the main features of our method is the ability to run in two different modes

1. Geometry-aware mode - learn manifold geometry and dynamics
2. Geometry-agnostic mode - learn dynamics only
1. Embedding-aware mode - learn manifold embedding and dynamics
2. Embedding-agnostic mode - learn dynamics only

To enable geometry-agnostic mode, set `inner_product_features=True` in training `params`. This engages an additional layer in the network after the computation of gradients, which makes them rotation invariant.
To enable embedding-agnostic mode, set `inner_product_features=True` in training `params`. This engages an additional layer in the network after the computation of gradients, which makes them rotation invariant.

As a slight cost of expressivity, this feature enables the orientation- and geometry-independent representation of dynamics over the manifolds. Amongst others, this allows one to recognise similar dynamics across different manifolds. See [RNN example](https://github.com/agosztolai/MARBLE/blob/main/examples/RNN/RNN.ipynb) for an illustration.
As a slight cost of expressivity, this feature enables the orientation- and embedding-independent representation of dynamics over the manifolds. Amongst others, this allows one to recognise similar dynamics across different manifolds. See [RNN example](https://github.com/agosztolai/MARBLE/blob/main/examples/RNN/RNN.ipynb) for an illustration.


## Troubleshooting guide
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