# The Monte Carlo Transformer: a stochastic self-attention model for sequence prediction

This paper introduces the Sequential Monte Carlo Transformer, an original approach that naturally captures the observations distribution in a recurrent architecture. The keys, queries, values and attention vectors of the network are considered as the unobserved stochastic states of its hidden structure. This generative model is such that at each time step the received observation is a random function of these past states in a given attention window. In this general state-space setting, we use Sequential Monte Carlo methods to approximate the posterior distributions of the states given the observations, and then to estimate the gradient of the log-likelihood. We thus propose a generative model providing a predictive distribution, instead of a single-point estimate.

• 2 publications
• 10 publications
• 23 publications
• 14 publications
• 60 publications
02/04/2022

### De-Sequentialized Monte Carlo: a parallel-in-time particle smoother

Particle smoothers are SMC (Sequential Monte Carlo) algorithms designed ...
05/13/2019

### Replica Conditional Sequential Monte Carlo

We propose a Markov chain Monte Carlo (MCMC) scheme to perform state inf...
06/24/2019

### Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation

Recent work in variational inference (VI) uses ideas from Monte Carlo es...
02/22/2018

### Deep learning algorithm for data-driven simulation of noisy dynamical system

We present a deep learning model, DE-LSTM, for the simulation of a stoch...
01/30/2022

### Fast Monte-Carlo Approximation of the Attention Mechanism

We introduce Monte-Carlo Attention (MCA), a randomized approximation met...
06/24/2011

### Monte Carlo Methods for Tempo Tracking and Rhythm Quantization

We present a probabilistic generative model for timing deviations in exp...
07/09/2020

### Online Approximate Bayesian learning

We introduce in this work a new method for online approximate Bayesian l...