changes in README files

AUTHORS.md

@@ -45,6 +45,9 @@ To contributors: please add your name to the list when you submit a patch to the
 * **[Aaron He](https://github.com/AaronHeee)**
    * Reco utils of NCF
    * Deep dive notebook demonstrating the use of NCF
+* **[Alexandros Ioannou](https://github.com/aioannou96)**
+   * Standard VAE algorithm 
+   * Multinomial VAE algorithm  
 * **[Bamdev Mishra](https://github.com/bamdevm)**
    * RLRMC algorithm
    * GeoIMC algorithm
@@ -56,6 +59,9 @@ To contributors: please add your name to the list when you submit a patch to the
    * SAR PySpark improvement
 * **[Daniel Schneider](https://github.com/danielsc)**
    * FastAI notebook
+* **[Evgenia Chroni](https://github.com/EvgeniaChroni)**
+   * Multinomial VAE algorithm
+   * Standard VAE algorithm
 * **[Gianluca Campanella](https://github.com/gcampanella)**
    * Spark optimization and support
 * **[Heather Spetalnick (Shapiro)](https://github.com/heatherbshapiro)**

README.md

@@ -78,6 +78,7 @@ The table below lists the recommender algorithms currently available in the repo
 | LightGBM/Gradient Boosting Tree<sup>*</sup> | [Python CPU](examples/00_quick_start/lightgbm_tinycriteo.ipynb) / [PySpark](examples/02_model_content_based_filtering/mmlspark_lightgbm_criteo.ipynb) | Content-Based Filtering | Gradient Boosting Tree algorithm for fast training and low memory usage in content-based problems |
 | LightGCN | [Python CPU / Python GPU](examples/02_model_collaborative_filtering/lightgcn_deep_dive.ipynb) | Collaborative Filtering | Deep learning algorithm with simplifies the design of GCN for predicting implicit feedback |
 | GRU4Rec | [Python CPU / Python GPU](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) | Collaborative Filtering | Sequential-based algorithm that aims to capture both long and short-term user preferences using recurrent neural networks |
+| Multinomial VAE | [Python CPU / Python GPU](examples/02_model_collaborative_filtering/multi_vae_deep_dive.ipynb) | Collaborative Filtering | Generative Model for predicting user/item interactions |
 | Neural Recommendation with Long- and Short-term User Representations (LSTUR)<sup>*</sup> | [Python CPU / Python GPU](examples/00_quick_start/lstur_MIND.ipynb) | Content-Based Filtering | Neural recommendation algorithm with long- and short-term user interest modeling |
 | Neural Recommendation with Attentive Multi-View Learning (NAML)<sup>*</sup> | [Python CPU / Python GPU](examples/00_quick_start/naml_MIND.ipynb) | Content-Based Filtering | Neural recommendation algorithm with attentive multi-view learning |
 | Neural Collaborative Filtering (NCF) | [Python CPU / Python GPU](examples/00_quick_start/ncf_movielens.ipynb) | Collaborative Filtering | Deep learning algorithm with enhanced performance for implicit feedback |
@@ -88,6 +89,7 @@ The table below lists the recommender algorithms currently available in the repo
 | Riemannian Low-rank Matrix Completion (RLRMC)<sup>*</sup> | [Python CPU](examples/00_quick_start/rlrmc_movielens.ipynb) | Collaborative Filtering | Matrix factorization algorithm using Riemannian conjugate gradients optimization with small memory consumption. |
 | Simple Algorithm for Recommendation (SAR)<sup>*</sup> | [Python CPU](examples/00_quick_start/sar_movielens.ipynb) | Collaborative Filtering | Similarity-based algorithm for implicit feedback dataset |
 | Short-term and Long-term preference Integrated Recommender (SLi-Rec)<sup>*</sup> | [Python CPU / Python GPU](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) | Collaborative Filtering | Sequential-based algorithm that aims to capture both long and short-term user preferences using attention mechanism, a time-aware controller and a content-aware controller |
+| Standard VAE | [Python CPU / Python GPU](examples/02_model_collaborative_filtering/standard_vae_deep_dive.ipynb) | Collaborative Filtering | Generative Model for predicting user/item interactions |
 | Surprise/Singular Value Decomposition (SVD) | [Python CPU](examples/02_model_collaborative_filtering/surprise_svd_deep_dive.ipynb) | Collaborative Filtering | Matrix factorization algorithm for predicting explicit rating feedback in datasets that are not very large |
 | Term Frequency - Inverse Document Frequency (TF-IDF) | [Python CPU](examples/00_quick_start/tfidf_covid.ipynb) | Content-Based Filtering | Simple similarity-based algorithm for content-based recommendations with text datasets |
 | Vowpal Wabbit (VW)<sup>*</sup> | [Python CPU (online training)](examples/02_model_content_based_filtering/vowpal_wabbit_deep_dive.ipynb) | Content-Based Filtering | Fast online learning algorithms, great for scenarios where user features / context are constantly changing |

examples/02_model_collaborative_filtering/README.md

@@ -8,8 +8,10 @@ In this directory, notebooks are provided to give a deep dive of collaborative f
 | [baseline_deep_dive](baseline_deep_dive.ipynb) | --- | Deep dive on baseline performance estimation.
 | [cornac_bpr_deep_dive](cornac_bpr_deep_dive.ipynb) | Python CPU | Deep dive on the BPR algorithm and implementation.
 | [lightgcn_deep_dive](lightgcn_deep_dive.ipynb) | Python CPU, GPU | Deep dive on a LightGCN algorithm and implementation.
+| [multi_vae_deep_dive](multi_vae_deep_dive.ipynb) | Python CPU, GPU | Deep dive on the Multinomial VAE algorithm and implementation.
 | [rbm_deep_dive](rbm_deep_dive.ipynb)| Python CPU, GPU | Deep dive on the rbm algorithm and its implementation.
 | [sar_deep_dive](sar_deep_dive.ipynb) | Python CPU | Deep dive on the SAR algorithm and implementation.
+| [standard_vae_deep_dive](standard_vae_deep_dive.ipynb) | Python CPU, GPU | Deep dive on the Standard VAE algorithm and implementation.
 | [surprise_svd_deep_dive](surprise_svd_deep_dive.ipynb) | Python CPU | Deep dive on a SVD algorithm and implementation.
 
 Details on model training are best found inside each notebook.

reco_utils/recommender/vae/standard_vae.py

@@ -263,7 +263,7 @@ class StandardVAE:
             self.beta = beta
       
         # Compute total annealing steps
-        self.total_anneal_steps = (self.number_of_batches * (self.n_epochs - int(self.n_epochs * 0.2))) 
+        self.total_anneal_steps = (self.number_of_batches * (self.n_epochs - int(self.n_epochs * 0.2))) // self.anneal_cap
 
         # Dropout parameters
         self.drop_encoder = drop_encoder