219 citations · 224 across the 3 of their papers we have counts for
6 papers
Debiasing Convolutional Neural Networks via Meta Orthogonalization
Kurtis Evan David, Qiang Liu, Ruth Fong
While deep learning models often achieve strong task performance, their successes are hampered by their inability to disentangle spurious correlations from causative factors, such…
Contextual Semantic Interpretability
Diego Marcos, Ruth Fong, Sylvain Lobry +3
Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpre…
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Miles Brundage, Shahar Avin, Jasmine Wang +56
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations an…
There and Back Again: Revisiting Backpropagation Saliency Methods
Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji +1
Saliency methods seek to explain the predictions of a model by producing an importance map across each input sample. A popular class of such methods is based on backpropagating a s…
NormGrad: Finding the Pixels that Matter for Training
Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji +2
The different families of saliency methods, either based on contrastive signals, closed-form formulas mixing gradients with activations or on perturbation masks, all focus on which…
Understanding Deep Networks via Extremal Perturbations and Smooth Masks
Ruth Fong, Mandela Patrick, Andrea Vedaldi
The problem of attribution is concerned with identifying the parts of an input that are responsible for a model's output. An important family of attribution methods is based on mea…