From the 1 of 31 linked papers with an AI index.
2 citations · 3 across the 10 of their papers we have counts for
6 papers · 1 filter
Hyperspherical Forward-Forward with Prototypical Representations
Shalini Sarode, Brian Moser, Joachim Folz +4
The Forward-Forward (FF) algorithm presents a compelling, bio-inspired alternative to backpropagation. However, while efficient in training, it has a computationally prohibitive in…
A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances
Brian B. Moser, Arundhati S. Shanbhag, Stanislav Frolov +3
Coreset selection targets the challenge of finding a small, representative subset of a large dataset that preserves essential patterns for effective machine learning. Although seve…
PRISM: Diversifying Dataset Distillation by Decoupling Architectural Priors
Brian B. Moser, Shalini Sarode, Federico Raue +6
Dataset distillation (DD) promises compact yet faithful synthetic data, but existing approaches often inherit the inductive bias of a single teacher model. As dataset size increase…
HyperCore: Coreset Selection under Noise via Hypersphere Models
Brian B. Moser, Arundhati S. Shanbhag, Tobias C. Nauen +4
The goal of coreset selection methods is to identify representative subsets of datasets for efficient model training. Yet, existing methods often ignore the possibility of annotati…
SubZeroCore: A Submodular Approach with Zero Training for Coreset Selection
Brian B. Moser, Tobias C. Nauen, Arundhati S. Shanbhag +4
The goal of coreset selection is to identify representative subsets of datasets for efficient model training. Yet, existing approaches paradoxically require expensive training-base…
FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data
Ahmed Anwar, Brian Moser, Dayananda Herurkar +4
The emergence of federated learning (FL) presents a promising approach to leverage decentralized data while preserving privacy. Furthermore, the combination of FL and anomaly detec…