Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time

12. Červenec 2020

Řečníci

O prezentaci

Recurrent neural networks (RNN) as used in machine learning are commonly formulated in discrete time, i.e. as recursive maps. This brings a lot of advantages for training models on data, e.g. for the purpose of time series prediction or dynamical systems identification, as powerful and efficient inference algorithms exist for discrete time systems and numerical integration of differential equations is not necessary. On the other hand, mathematical analysis of dynamical systems inferred from data is often more convenient and enables additional insights if these are formulated in continuous time, i.e. as systems of ordinary (or partial) differential equations (ODE). Here we show how to perform such a translation from discrete to continuous time for a particular class of ReLU-based RNN. We prove three theorems on the mathematical equivalence between the discrete and continuous time formulations under a variety of conditions, and illustrate how to use our mathematical results on different machine learning and nonlinear dynamical systems examples.

Organizátor

Kategorie

O organizátorovi (ICML 2020)

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

Uložení prezentace

Měla by být tato prezentace uložena po dobu 1000 let?

Jak ukládáme prezentace

Pro uložení prezentace do věčného trezoru hlasovalo 0 diváků, což je 0.0 %

Sdílení

Doporučená videa

Prezentace na podobné téma, kategorii nebo přednášejícího

Zajímají Vás podobná videa? Sledujte ICML 2020