Approximate dynamic programming for constrained linear systems: A piecewise quadratic approximation approach

Approximate dynamic programming (ADP) faces challenges in dealing with constraints in control problems. Model predictive control (MPC) is, in comparison, well-known for its accommodation of constraints and stability guarantees, although its computation is sometimes prohibitive. This paper introduces an approach combining the two methodologies...

Interpretable Deep Visual Place Recognition

We propose a framework to interpret deep convolutional models for visual place classification. Given a deep place classification model, our proposed method produces visual explanations and saliency maps that reveal the understanding of images by the model. To evaluate the interpretability, t-SNE algorithm is used for mapping and visualization of...

Sight-seeing in the eyes of deep neural networks

We address the interpretability of convolutional neural networks (CNNs) for predicting a geo-location from an image. In a pilot experiment we classify images of Pittsburgh vs Tokyo and visualize the learned CNN filters. We found that varying the CNN architecture leads to variating in the visualized filters. This calls for further...

Language Models With Meta-information

Language modeling plays a critical role in natural language processing and understanding. Starting from a general structure, language models are able to learn natural language patterns from rich input data. However, the state-of-the-art language models only take advantage of words themselves, which are not sufficient to characterize the language...