Search Results for author:
Found 7 papers, 4 papers with code
SARC: Soft Actor Retrospective Critic
The two-time scale nature of SAC, which is an actor-critic algorithm, is characterised by the fact that the critic estimate has not converged for the actor at any given time, but since the critic learns faster than the actor, it ensures eventual consistency between the two.
Information-theoretic Evolution of Model Agnostic Global Explanations
Our approach builds on top of existing local model explanation methods to extract conditions important for explaining model behavior for specific instances followed by an evolutionary algorithm that optimizes an information theory based fitness function to construct rules that explain global model behavior.
MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance
Training a classification model on a dataset where the instances of one class outnumber those of the other class is a challenging problem.
Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency
We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that our approach generates saliency maps that are more interpretable for humans than existing approaches.
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution
We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that SARFA generates saliency maps that are more interpretable for humans than existing approaches.
Extractive Summarization using Deep Learning
We are exploring various features to improve the set of sentences selected for the summary, and are using a Restricted Boltzmann Machine to enhance and abstract those features to improve resultant accuracy without losing any important information.
MAGIX: Model Agnostic Globally Interpretable Explanations
Explaining the behavior of a black box machine learning model at the instance level is useful for building trust.
