Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment

Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-Grams

Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches

Stochastic neural net weights are used in a variety of contexts, including regularization, Bayesian neural nets, exploration in reinforcement learning, and evolution strategies. Unfortunately, due to the large number of …

From chocolate bunny to chocolate crocodile: Do Language Models Understand Noun Compounds?

Noun compound interpretation is the task of expressing a noun compound (e.g. chocolate bunny) in a free-text paraphrase that makes the relationship between the constituent nouns explicit (e.g. bunny-shaped chocolate). We…

Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks

In this paper, we present the first study that compares different models of Bayesian Neural Networks (BNNs) to predict the posterior distribution of the cosmological parameters directly from the Cosmic Microwave Backgrou…

Comparing Bayesian Models for Organ Contouring in Head and Neck Radiotherapy

Deep learning models for organ contouring in radiotherapy are poised for clinical usage, but currently, there exist few tools for automated quality assessment (QA) of the predicted contours. Using Bayesian models and the…