910 research outputs found
Self-Driving Cars: Exploring the Potential of Using Convolutional Neural Network to Overcome Road Variation
The use of self-driving cars can benefit the society in many ways, such as reducing traffic accidents and enabling disabled people to travel independently. The potential of reducing traffic accidents can be considered most important, since in 2017, mistakes made by human drivers were the cause of over 90% of the traffic accidents, leading to 40,100 people’s deaths in the United States. If human drivers were replaced by autonomous systems, the number of traffic accidents would decrease. Although the concept of self-driving car was raised since at least the 1920s, a commonly accepted development of self-driving car has not yet appeared. A significant challenge is the creation of a system that can accurately detect the environment around itself and then form the right driving command. Recent progress in deep learning suggested that convolutional neural networks are a form of machine learning that can be trained to extract features and use those features to control a car. This project focuses on extending the network model in the paper published by NVIDA in 2016. The aim of the project is to evaluate how well a convolutional neural network could perform on a simple, simulated roadway with road varying and missing road edges
Improve Long-term Memory Learning Through Rescaling the Error Temporally
This paper studies the error metric selection for long-term memory learning
in sequence modelling. We examine the bias towards short-term memory in
commonly used errors, including mean absolute/squared error. Our findings show
that all temporally positive-weighted errors are biased towards short-term
memory in learning linear functionals. To reduce this bias and improve
long-term memory learning, we propose the use of a temporally rescaled error.
In addition to reducing the bias towards short-term memory, this approach can
also alleviate the vanishing gradient issue. We conduct numerical experiments
on different long-memory tasks and sequence models to validate our claims.
Numerical results confirm the importance of appropriate temporally rescaled
error for effective long-term memory learning. To the best of our knowledge,
this is the first work that quantitatively analyzes different errors' memory
bias towards short-term memory in sequence modelling.Comment: 12 pages, 7 figure
Inverse Approximation Theory for Nonlinear Recurrent Neural Networks
We prove an inverse approximation theorem for the approximation of nonlinear
sequence-to-sequence relationships using RNNs. This is a so-called
Bernstein-type result in approximation theory, which deduces properties of a
target function under the assumption that it can be effectively approximated by
a hypothesis space. In particular, we show that nonlinear sequence
relationships, viewed as functional sequences, that can be stably approximated
by RNNs with hardtanh/tanh activations must have an exponential decaying memory
structure -- a notion that can be made precise. This extends the previously
identified curse of memory in linear RNNs into the general nonlinear setting,
and quantifies the essential limitations of the RNN architecture for learning
sequential relationships with long-term memory. Based on the analysis, we
propose a principled reparameterization method to overcome the limitations. Our
theoretical results are confirmed by numerical experiments
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