1.6k citations · 2k across the 16 of their papers we have counts for
17 papers
Mining of Single-Class by Active Learning for Semantic Segmentation
Hugues Lambert, Emma Slade
Several Active Learning (AL) policies require retraining a target model several times in order to identify the most informative samples and rarely offer the option to focus on the…
Deep reinforced active learning for multi-class image classification
Emma Slade, Kim M. Branson
High accuracy medical image classification can be limited by the costs of acquiring more data as well as the time and expertise needed to label existing images. In this paper, we a…
GNisi: A graph network for reconstructing Ising models from multivariate binarized data
Emma Slade, Sonya Kiselgof, Lena Granovsky +1
Ising models are a simple generative approach to describing interacting binary variables. They have proven useful in a number of biological settings because they enable one to repr…
Data efficiency in graph networks through equivariance
Francesco Farina, Emma Slade
We introduce a novel architecture for graph networks which is equivariant to any transformation in the coordinate embeddings that preserves the distance between neighbouring nodes.…
Cuts for two-body decays at colliders
Gavin P. Salam, Emma Slade
Fixed-order perturbative calculations of fiducial cross sections for two-body decay processes at colliders show disturbing sensitivity to unphysically low momentum scales and, in t…
Symmetry-driven graph neural networks
Francesco Farina, Emma Slade
Exploiting symmetries and invariance in data is a powerful, yet not fully exploited, way to achieve better generalisation with more efficiency. In this paper, we introduce two grap…