Probabilistic Models and Label/Feature Distribution smoothing

This release fixes some minor bugs but mainly brings a couple of new functionalities:

1. New experimental Attentive models, namely: ContextAttentionMLP and SelfAttentionMLP.
2. 2 Probabilistic models based on Bayes by Backprop (BBP) as described in Weight Uncertainty in Neural Networks, namely: BayesianTabMlp and BayesianWide.
3. Label and Feature Distribution Smoothing (LDS and FDS) for Deep Imbalanced Regression (DIR) as described in Delving into Deep Imbalanced Regression
4. Better integration with torchvision for the deepimage component of a WideDeep model
5. 3 Available models for the deeptext component of a WideDeep model. Namely: BasicRNN, AttentiveRNN and StackedAttentiveRNN