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Found 5 papers, 1 papers with code
CrystalBox: Future-Based Explanations for Input-Driven Deep RL Systems
We present CrystalBox, a novel, model-agnostic, posthoc explainability framework for Deep Reinforcement Learning (DRL) controllers in the large family of input-driven environments which includes computer systems.
Plume: A Framework for High Performance Deep RL Network Controllers via Prioritized Trace Sampling
First, we identify the critical features that determine the behavior of the traces.
Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 Methods
We incorporated 24 Feature Subset Selection (FSS) algorithms, including multivariate and univariate, supervised and unsupervised, wrappers and filters.
Gene family amplification facilitates adaptation in freshwater Unionid bivalve Megalonaias nervosa
With advances in genome sequencing it is possible to gain a broader view of how gene family proliferation and TE content evolve in non-model species when populations become threatened.
3D Topology Optimization using Convolutional Neural Networks
Topology optimization is computationally demanding that requires the assembly and solution to a finite element problem for each material distribution hypothesis.
