30 citations · 80 across the 30 of their papers we have counts for
5 papers · 1 filter
Unlearning via Sparse Representations
Vedant Shah, Frederik Träuble, Ashish Malik +5
Machine \emph{unlearning}, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infeasible by existing techniques. We propose…
On the Foundations of Shortcut Learning
Katherine L. Hermann, Hossein Mobahi, Thomas Fel +1
Deep-learning models can extract a rich assortment of features from data. Which features a model uses depends not only on \emph{predictivity} -- how reliably a feature indicates tr…
Can Neural Network Memorization Be Localized?
Pratyush Maini, Michael C. Mozer, Hanie Sedghi +3
Recent efforts at explaining the interplay of memorization and generalization in deep overparametrized networks have posited that neural networks "hard" example…
Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior
Ayush Chakravarthy, Trang Nguyen, Anirudh Goyal +2
The aim of object-centric vision is to construct an explicit representation of the objects in a scene. This representation is obtained via a set of interchangeable modules called \…
DiscoGen: Learning to Discover Gene Regulatory Networks
Nan Rosemary Ke, Sara-Jane Dunn, Jorg Bornschein +11
Accurately inferring Gene Regulatory Networks (GRNs) is a critical and challenging task in biology. GRNs model the activatory and inhibitory interactions between genes and are inhe…