Adjustments to intros and conclusions

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

thesis.bib

@@ -1398,6 +1398,7 @@
   pages = {651--686},
   issn = {05503213},
   doi = {10.1016/0550-3213(90)90379-R},
+  file = {/home/riccardo/.local/share/zotero/files/di_bartolomeo_et_al_1990_general_properties_of_vertices_with_two_ramond_or_twisted_states4.pdf},
   keywords = {archived},
   langid = {english},
   number = {3}
@@ -1464,6 +1465,7 @@
   pages = {63--70},
   issn = {03702693},
   doi = {10.1016/0370-2693(90)90098-Q},
+  file = {/home/riccardo/.local/share/zotero/files/di_vecchia_et_al_1990_a_vertex_including_emission_of_spin_fields4.pdf},
   keywords = {archived},
   langid = {english},
   number = {1-2}
@@ -2093,7 +2095,8 @@
   date = {2014},
   pages = {2672--2680},
   publisher = {{Curran Associates, Inc.}},
-  url = {http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf}
+  url = {http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf},
+  file = {/home/riccardo/.local/share/zotero/files/goodfellow_et_al_2014_generative_adversarial_nets.pdf}
 }
 
 @inproceedings{Gori:2005:NewModelLearning,
@@ -2104,6 +2107,7 @@
   volume = {2},
   pages = {729--734},
   doi = {10.1109/IJCNN.2005.1555942},
+  file = {/home/riccardo/.local/share/zotero/files/gori_et_al_2005_a_new_model_for_learning_in_graph_domains2.pdf},
   organization = {{IEEE}}
 }
 
@@ -2142,7 +2146,7 @@
 @article{Gray:2013:AllCompleteIntersection,
   title = {All {{Complete Intersection Calabi}}-{{Yau Four}}-{{Folds}}},
   author = {Gray, James and Haupt, Alexander S. and Lukas, Andre},
-  date = {2013-07},
+  date = {2013},
   journaltitle = {Journal of High Energy Physics},
   shortjournal = {J. High Energ. Phys.},
   volume = {2013},
@@ -2160,7 +2164,7 @@
 @article{Gray:2014:TopologicalInvariantsFibration,
   title = {Topological {{Invariants}} and {{Fibration Structure}} of {{Complete Intersection Calabi}}-{{Yau Four}}-{{Folds}}},
   author = {Gray, James and Haupt, Alexander S. and Lukas, Andre},
-  date = {2014-09},
+  date = {2014},
   journaltitle = {Journal of High Energy Physics},
   shortjournal = {J. High Energ. Phys.},
   volume = {2014},
@@ -2747,7 +2751,7 @@
 @online{Kingma:2014:AutoEncodingVariationalBayes,
   title = {Auto-{{Encoding Variational Bayes}}},
   author = {Kingma, Diederik P. and Welling, Max},
-  date = {2014-05-01},
+  date = {2014},
   url = {http://arxiv.org/abs/1312.6114},
   urldate = {2020-10-10},
   abstract = {How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.},
@@ -3491,7 +3495,7 @@
 @online{Rezende:2014:StochasticBackpropagationApproximate,
   title = {Stochastic {{Backpropagation}} and {{Approximate Inference}} in {{Deep Generative Models}}},
   author = {Rezende, Danilo Jimenez and Mohamed, Shakir and Wierstra, Daan},
-  date = {2014-05-30},
+  date = {2014},
   url = {http://arxiv.org/abs/1401.4082},
   urldate = {2020-10-10},
   abstract = {We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation.},
@@ -3566,6 +3570,7 @@
   author = {Salimans, Tim and Kingma, Diederik and Welling, Max},
   date = {2015},
   pages = {1218--1226},
+  file = {/home/riccardo/.local/share/zotero/files/salimans_et_al_2015_markov_chain_monte_carlo_and_variational_inference.pdf},
   keywords = {⛔ No DOI found}
 }
 
@@ -3575,8 +3580,9 @@
   author = {Scarselli, Franco and Tsoi, Ah Chung and Gori, Marco and Hagenbuchner, Markus},
   date = {2004},
   pages = {42--56},
-  keywords = {⛔ No DOI found},
-  organization = {{Springer}}
+  doi = {10.1007/978-3-540-27868-9_4},
+  file = {/home/riccardo/.local/share/zotero/files/scarselli_et_al_2004_graphical-based_learning_environments_for_pattern_recognition3.pdf},
+  isbn = {978-3-540-22570-6 978-3-540-27868-9}
 }
 
 @online{Schellekens:2017:BigNumbersString,