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Found 8 papers, 4 papers with code
CDT: Cascading Decision Trees for Explainable Reinforcement Learning
As a second contribution our study reveals limitations of explaining black-box policies via imitation learning with tree-based explainable models, due to its inherent instability.
Cascaded Deep Neural Networks for Retinal Layer Segmentation of Optical Coherence Tomography with Fluid Presence
Optical coherence tomography (OCT) is a non-invasive imaging technology which can provide micrometer-resolution cross-sectional images of the inner structures of the eye.
On the Effectiveness of Low Frequency Perturbations
Carefully crafted, often imperceptible, adversarial perturbations have been shown to cause state-of-the-art models to yield extremely inaccurate outputs, rendering them unsuitable for safety-critical application domains.
On the Sensitivity of Adversarial Robustness to Input Data Distributions
Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarial trained model that is both trained and evaluated on the new distribution.
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
advertorch is a toolbox for adversarial robustness research.
MMA Training: Direct Input Space Margin Maximization through Adversarial Training
We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary.
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds
In this paper, we investigate Dimensionality reduction (DR) maps in an information retrieval setting from a quantitative topology point of view.
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
We propose a novel regularizer to improve the training of Generative Adversarial Networks (GANs).
