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Found 5 papers, 2 papers with code
Safe Predictors for Enforcing Input-Output Specifications
We present an approach for designing correct-by-construction neural networks (and other machine learning models) that are guaranteed to be consistent with a collection of input-output specifications before, during, and after algorithm training.
Kernelized Capsule Networks
Capsule Networks attempt to represent patterns in images in a way that preserves hierarchical spatial relationships.
Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning
Decision trees are ubiquitous in machine learning for their ease of use and interpretability.
Learning Robust Representations for Automatic Target Recognition
Radio frequency (RF) sensors are used alongside other sensing modalities to provide rich representations of the world.
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem.
