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Found 7 papers, 3 papers with code
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting
Through extensive experiments, we describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability.
On the Explanation of Similarity for Developing and Deploying CBR Systems
During the early stages of developing Case-Based Reasoning (CBR) systems the definition of similarity measures is challenging since this task requires transferring implicit knowledge of domain experts into knowledge representations.
FishNet: A Unified Embedding for Salmon Recognition
In this paper we propose FishNet, based on a deep learning technique that has been successfully used for identifying humans, to identify salmon. We create a dataset of labeled fish images and then test the performance of the FishNet architecture.
Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning
In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value.
Learning similarity measures from data
The main motivation for this work is to automate the construction of similarity measures using machine learning, while keeping training time as low as possible.
Similarity Measure Development for Case-Based Reasoning- A Data-driven Approach
In this paper, we demonstrate a data-driven methodology for modelling the local similarity measures of various attributes in a dataset.
Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning
Reinforcement learning (RL) is capable of managing wireless, energy-harvesting IoT nodes by solving the problem of autonomous management in non-stationary, resource-constrained settings.
