Deep learning-based vehicle behavior prediction for autonomous driving applications: A review
Behaviour prediction function of an autonomous vehicle predicts the future states of the
nearby vehicles based on the current and past observations of the surrounding environment …
nearby vehicles based on the current and past observations of the surrounding environment …
[HTML][HTML] Short-term bitcoin market prediction via machine learning
We analyze the predictability of the bitcoin market across prediction horizons ranging from 1
to 60 min. In doing so, we test various machine learning models and find that, while all …
to 60 min. In doing so, we test various machine learning models and find that, while all …
Deep learning: RNNs and LSTM
Recurrent neural networks (RNNs) are a class of neural networks that are naturally suited to
processing time-series data and other sequential data. Here we introduce recurrent neural …
processing time-series data and other sequential data. Here we introduce recurrent neural …
[LIBRO][B] Supervised sequence labelling
A Graves, A Graves - 2012 - Springer
This chapter provides the background material and literature review for supervised
sequence labelling. Section 2.1 briefly reviews supervised learning in general. Section 2.2 …
sequence labelling. Section 2.1 briefly reviews supervised learning in general. Section 2.2 …
Particle swarm optimization performance improvement using deep learning techniques
Deep learning is widely used to automate processes, improve performance, detect patterns,
and solve problems. Thus, applications of deep learning are limitless. Particle swarm …
and solve problems. Thus, applications of deep learning are limitless. Particle swarm …
Temporal models for predicting student dropout in massive open online courses
Over the past few years, the rapid emergence of massive open online courses (MOOCs) has
sparked a great deal of research interest in MOOC data analytics. Dropout prediction, or …
sparked a great deal of research interest in MOOC data analytics. Dropout prediction, or …
Learning stochastic recurrent networks
Leveraging advances in variational inference, we propose to enhance recurrent neural
networks with latent variables, resulting in Stochastic Recurrent Networks (STORNs). The …
networks with latent variables, resulting in Stochastic Recurrent Networks (STORNs). The …
Path neural networks: Expressive and accurate graph neural networks
Graph neural networks (GNNs) have recently become the standard approach for learning
with graph-structured data. Prior work has shed light into their potential, but also their …
with graph-structured data. Prior work has shed light into their potential, but also their …
Recurrent neural networks are universal approximators
AM Schäfer, HG Zimmermann - … , Athens, Greece, September 10-14, 2006 …, 2006 - Springer
Neural networks represent a class of functions for the efficient identification and forecasting
of dynamical systems. It has been shown that feedforward networks are able to approximate …
of dynamical systems. It has been shown that feedforward networks are able to approximate …
Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task
Primates can richly parse sensory inputs to infer latent information. This ability is
hypothesized to rely on establishing mental models of the external world and running mental …
hypothesized to rely on establishing mental models of the external world and running mental …