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Found 10 papers, 0 papers with code
Visual Transformers for Primates Classification and Covid Detection
We apply the vision transformer, a deep machine learning model build around the attention mechanism, on mel-spectrogram representations of raw audio recordings.
Capturing Dependencies within Machine Learning via a Formal Process Model
We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way.
Quantifying Multimodality in World Models
In this work, we analyze existing and propose new metrics for the detection and quantification of multimodal uncertainty in RL based World Models.
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning
A characteristic of reinforcement learning is the ability to develop unforeseen strategies when solving problems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms
In many fields of research, labeled datasets are hard to acquire.
Policy Entropy for Out-of-Distribution Classification
One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained.
Bayesian Surprise in Indoor Environments
Agents of LBS, such as mobile robots or non-player characters in computer games, may use the context surprise to focus more on important regions of a map for a better use or understanding of the floor plan.
Trajectory annotation using sequences of spatial perception
In the near future, more and more machines will perform tasks in the vicinity of human spaces or support them directly in their spatially bound activities.
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning
We further present a first viable solution for calculating a dynamic classification threshold, based on the uncertainty distribution of the training data.
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning
Although prior work has shown that dropout-based variational inference techniques and bootstrap-based approaches can be used to model epistemic uncertainty, the suitability for detecting OOD samples in deep reinforcement learning remains an open question.
