Mar 24, 2024 · Then, the diffusion-based conditional generator is able to efficiently generate realistic multivariate timestamp values on a continuous latent ...

Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design - automl/ProbTransformer.

We propose a hierarchical latent distribution to enhance one of the most successful deep learning models, the Transformer, to accommodate ambiguities and data ...

This paper proposes a Transformer-based probabilistic residential net load forecasting method that utilizes quantile regression to quantify uncertainty in ...

The self-attention mechanism of a Transformer is used to capture the complex and long-term dynamics of the complex actions. By explicitly modeling the ...

Best Probabilistic Transformers. 363. Related Work. While in [10,11,12,13] ideas from abstract interpretation have been applied to probabilistic models, to ...

We propose Transformer with a Mixture of Gaussian Keys (Transformer-MGK), a novel transformer architecture that replaces redundant heads in transformers with a ...

Abstract. Probabilistic predicates generalize standard predicates over a state space; with probabilistic predicate transformers one thus reasons about ...

Improving Transformers with Probabilistic Attention Keys. Figure 1. Training loss and test accuracy of Transformer-MGK/MLK vs. softmax/linear transformer on ...