Probabilistic approach to reliable localizationNaokiAkai2
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This document summarizes the work of Naoki Akai on developing a probabilistic approach to reliable localization. It presents a new graphical model that estimates a robot's pose, sensor measurements, and localization correctness. This model allows for robust localization in dynamic environments, immediate failure detection based on estimated reliability, and quick re-localization after failures. It is implemented in als_ros, an open-source ROS package for LiDAR-based localization.
The document discusses Latent Dirichlet Allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. It provides examples of LDA applied to documents and visualizations of the topic distributions. It also explores how changing the alpha parameter, which controls the topic distributions, affects the results. LDA is presented as a way to discover abstract "topics" that occur in a collection of documents.
Probabilistic approach to reliable localizationNaokiAkai2
?
This document summarizes the work of Naoki Akai on developing a probabilistic approach to reliable localization. It presents a new graphical model that estimates a robot's pose, sensor measurements, and localization correctness. This model allows for robust localization in dynamic environments, immediate failure detection based on estimated reliability, and quick re-localization after failures. It is implemented in als_ros, an open-source ROS package for LiDAR-based localization.
The document discusses Latent Dirichlet Allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. It provides examples of LDA applied to documents and visualizations of the topic distributions. It also explores how changing the alpha parameter, which controls the topic distributions, affects the results. LDA is presented as a way to discover abstract "topics" that occur in a collection of documents.
JMI Techtalk: ??? - Toward tf.keras from tf.estimator - From TensorFlow 2.0 p...Lablup Inc.
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? Techtalk??? TensorFlow 2.0?? ??? tf.estimator ?? tf.keras? ???? ?? ??? ??? ?????.
This Techtalk explains why you need to migrate from tf.estimator to tf.keras when moving to TensorFlow 2.0.
100% Serverless big data scale production Deep Learning Systemhoondong kim
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- BigData Sale Deep Learning Training System (with GPU Docker PaaS on Azure Batch AI)
- Deep Learning Serving Layer (with Auto Scale Out Mode on Web App for Linux Docker)
- BigDL, Keras, Tensorlfow, Horovod, TensorflowOnAzure
This document provides information about a dialog manager including a link to its source code on GitHub. It lists the main Python files that make up the dialog manager and references a paper on using character-level convolutional neural networks for sentence classification. It also mentions morphological analysis and training data, and shows sample execution results.
This document discusses dialog management systems and the Ravenclaw dialog manager. It describes Ravenclaw's 2-tier architecture with a dialog task specification (DTS) representing the hierarchical task structure and dialog agents. The core algorithm uses a dialog stack and expectation agenda to execute agents in two phases - execution and input. Context-based semantic disambiguation and mixed-initiative dialogs are supported through the expectation agenda. Error handling is also discussed.