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Found 13 papers, 0 papers with code
Depth Estimation using Weighted-loss and Transfer Learning
The optimized loss function is a combination of weighted losses to which enhance robustness and generalization: Mean Absolute Error (MAE), Edge Loss and Structural Similarity Index (SSIM).
Beyond the Known: Adversarial Autoencoders in Novelty Detection
The first is that we compute the novelty probability by linearizing the manifold that holds the structure of the inlier distribution.
Robust Classification of High-Dimensional Spectroscopy Data Using Deep Learning and Data Synthesis
This paper presents a new approach to classification of high dimensional spectroscopy data and demonstrates that it outperforms other current state-of-the art approaches.
A Virtual Testbed for Critical Incident Investigation with Autonomous Remote Aerial Vehicle Surveying, Artificial Intelligence, and Decision Support
Autonomous robotics and artificial intelligence techniques can be used to support human personnel in the event of critical incidents.
Using a Game Engine to Simulate Critical Incidents and Data Collection by Autonomous Drones
Using a game engine, we have developed a virtual environment which models important aspects of critical incident scenarios.
Skilled Experience Catalogue: A Skill-Balancing Mechanism for Non-Player Characters using Reinforcement Learning
The objective of this mechanism is to approximately match the skill level of an NPC to an opponent in real-time.
Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learning
In current state-of-the-art commercial first person shooter games, computer controlled bots, also known as non player characters, can often be easily distinguishable from those controlled by humans.
Analysis of the Effect of Unexpected Outliers in the Classification of Spectroscopy Data
Multi-class classification algorithms are very widely used, but we argue that they are not always ideal from a theoretical perspective, because they assume all classes are characterized by the data, whereas in many applications, training data for some classes may be entirely absent, rare, or statistically unrepresentative.
Learning to Shoot in First Person Shooter Games by Stabilizing Actions and Clustering Rewards for Reinforcement Learning
While reinforcement learning (RL) has been applied to turn-based board games for many years, more complex games involving decision-making in real-time are beginning to receive more attention.
DRE-Bot: A Hierarchical First Person Shooter Bot Using Multiple Sarsa(λ) Reinforcement Learners
This paper describes an architecture for controlling non-player characters (NPC) in the First Person Shooter (FPS) game Unreal Tournament 2004.
A Virtual Environment with Multi-Robot Navigation, Analytics, and Decision Support for Critical Incident Investigation
Because of the rare and high-risk nature of these events, realistic simulations can support the development and evaluation of AI-based tools.
One-Class Classification: Taxonomy of Study and Review of Techniques
In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied.
Probabilistic detection of short events, with application to critical care monitoring
We describe an application of probabilistic modeling and inference technology to the problem of analyzing sensor data in the setting of an intensive care unit (ICU).
