219 citations · 1.8k across the 99 of their papers we have counts for
18 papers · 1 filter
Sub-Linear Memory: How to Make Performers SLiM
Valerii Likhosherstov, Krzysztof Choromanski, Jared Davis +2
The Transformer architecture has revolutionized deep learning on sequential data, becoming ubiquitous in state-of-the-art solutions for a wide variety of applications. Yet vanilla…
Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training
Andrei Margeloiu, Nikola Simidjievski, Mateja Jamnik +1
We investigate the influence of adversarial training on the interpretability of convolutional neural networks (CNNs), specifically applied to diagnosing skin cancer. We show that g…
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Umang Bhatt, Javier Antorán, Yunfeng Zhang +12
Algorithmic transparency entails exposing system properties to various stakeholders for purposes that include understanding, improving, and contesting predictions. Until now, most…
Now You See Me (CME): Concept-based Model Extraction
Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik +2
Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering DNN-based approaches is improving their explainability. In th…
On the Fairness of Causal Algorithmic Recourse
Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt +3
Algorithmic fairness is typically studied from the perspective of predictions. Instead, here we investigate fairness from the perspective of recourse actions suggested to individua…
Rethinking Attention with Performers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan +10
We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed t…