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Found 5 papers, 4 papers with code
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives.
Fast Context Adaptation via Meta-Learning
We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable.
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
We build on previous work on Gaussian processes and pairwise comparisons for preference modelling, extend it to the multi-objective decision support scenario, and propose new ordered preference elicitation strategies based on ranking and clustering.
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input.
A New Method to Visualize Deep Neural Networks
We present a method for visualising the response of a deep neural network to a specific input.
