This phrase is from the "Rethinking the Inception Architecture for Computer Vision" paper. it says :

1. Higher dimensional representations are easier to process locally within a network. Increasing the activations per tile in a convolutional network allows for more disentangled features. The resulting networks will train faster.

What does the phrase

1. Higher dimensional representations are easier to process locally within a network"

mean?

Does it simply mean, a higher representation is achieved more easily by having more feature-maps in a layer? again indicating, having lots of feature-maps per layer, (more width) is more beneficial?
If so, does the next part indicate the same thing where it says :

Increasing the activations per tile in a convolutional network allows for more disentangled features.

what does disentagled features mean in that regard?