Search Results for author:
Found 11 papers, 4 papers with code
Bias Mitigation Framework for Intersectional Subgroups in Neural Networks
We propose a fairness-aware learning framework that mitigates intersectional subgroup bias associated with protected attributes.
Prescriptive and Descriptive Approaches to Machine-Learning Transparency
We further propose a preliminary approach, called Method Cards, which aims to increase the transparency and reproducibility of ML systems by providing prescriptive documentation of commonly-used ML methods and techniques.
A Tour of Visualization Techniques for Computer Vision Datasets
We survey a number of data visualization techniques for analyzing Computer Vision (CV) datasets.
Convolutional Networks are Inherently Foveated
When convolutional layers apply no padding, central pixels have more ways to contribute to the convolution than peripheral pixels.
Investigating sanity checks for saliency maps with image and text classification
Saliency maps have shown to be both useful and misleading for explaining model predictions especially in the context of images.
Investigating Saturation Effects in Integrated Gradients
We explore these effects and find that gradients in saturated regions of this path, where model output changes minimally, contribute disproportionately to the computed attribution.
Mind the Pad -- CNNs can Develop Blind Spots
We show how feature maps in convolutional networks are susceptible to spatial bias.
Captum: A unified and generic model interpretability library for PyTorch
The library contains generic implementations of a number of gradient and perturbation-based attribution algorithms, also known as feature, neuron and layer importance algorithms, as well as a set of evaluation metrics for these algorithms.
Visualizing Classification Structure of Large-Scale Classifiers
We propose a measure to compute class similarity in large-scale classification based on prediction scores.
Prediction Scores as a Window into Classifier Behavior
Most multi-class classifiers make their prediction for a test sample by scoring the classes and selecting the one with the highest score.
Do Convolutional Neural Networks Learn Class Hierarchy?
We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data.
