Update images and references

Signed-off-by: Riccardo Finotello <riccardo.finotello@gmail.com>

sec/abstract.tex

@@ -12,7 +12,7 @@ We finally present a new artificial intelligence approach to algebraic geometry
 We compute the Hodge numbers of Complete Intersection Calabi--Yau $3$-folds using deep learning techniques based on computer vision and object recognition techniques.
 We also include a methodological study of machine learning applied to data in string theory: as in most applications machine learning almost never relies on the blind application of algorithms to the data but it requires a careful exploratory analysis and feature engineering.
 We thus show how such an approach can help in improving results by processing the data before using it.
-We then show how deep learning can reach the highest accuracy in the task with smaller networks with less parameters.
+We then show that the deep learning approach can reach the highest accuracy in the task with smaller networks and less parameters.
 This is a novel approach to the task: differently from previous attempts we focus on using convolutional neural networks capable of reaching higher accuracy on the predictions and ensuring phenomenological relevance to results.
 In fact parameter sharing and concurrent scans of the configuration matrix retain better generalisation properties and adapt better to the task than fully connected networks.