Predicting the rules behind - Deep Symbolic Regression for Recurrent Sequences (w/ author interview)

#deeplearning #symbolic #research This video includes an interview with first author Stéphane d’Ascoli (). Deep neural networks are typically excellent at numeric regression, but using them for symbolic computation has largely been ignored so far. This paper uses transformers to do symbolic regression on integer and floating point number sequences, which means that given the start of a sequence of numbers, the model has to not only predict the correct continuation, but also predict the data generating formula behind the sequence. Through clever encoding of the input space and a well constructed training data generation process, this paper’s model can learn and represent many of the sequences in the OEIS, the online encyclopedia of integer sequences and it also features an interactive demo if you want to try it by yourself. OUTLINE: 0:00 - Introduction 2:20 - Summary of the Paper 16:10 - Start of Interview 17:15 - Why this research direction