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Found 7 papers, 3 papers with code
Conversational Agents: Theory and Applications
In this chapter, we provide a review of conversational agents (CAs), discussing chatbots, intended for casual conversation with a user, as well as task-oriented agents that generally engage in discussions intended to reach one or several specific goals, often (but not always) within a specific domain.
The five Is: Key principles for interpretable and safe conversational AI
In this position paper, we present five key principles, namely interpretability, inherent capability to explain, independent data, interactive learning, and inquisitiveness, for the development of conversational AI that, unlike the currently popular black box approaches, is transparent and accountable.
Model Learning with Personalized Interpretability Estimation (ML-PIE)
The latter is estimated by a neural network that is trained concurrently to the evolution using the feedback of the user, which is collected using uncertainty-based active learning.
Lidar-Camera Co-Training for Semi-Supervised Road Detection
Recent advances in the field of machine learning and computer vision have enabled the development of fast and accurate road detectors.
LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks
Whereas in the former two fusion approaches, the integration of multimodal information is carried out at a predefined depth level, the cross fusion FCN is designed to directly learn from data where to integrate information; this is accomplished by using trainable cross connections between the LIDAR and the camera processing branches.
LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks
The fully convolutional neural network trained using all the available sensors together with driving directions achieved the best MaxF score of 88. 13% when considering a region of interest of 60x60 meters.
Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks
The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps.
