4 citations · 4 across the 4 of their papers we have counts for
4 papers
DRoP: Distributionally Robust Data Pruning
Artem Vysogorets, Kartik Ahuja, Julia Kempe
In the era of exceptionally data-hungry models, careful selection of the training data is essential to mitigate the extensive costs of deep learning. Data pruning offers a solution…
Deconstructing the Goldilocks Zone of Neural Network Initialization
Artem Vysogorets, Anna Dawid, Julia Kempe
The second-order properties of the training loss have a massive impact on the optimization dynamics of deep learning models. Fort & Scherlis (2019) discovered that a large excess o…
ImpressLearn: Continual Learning via Combined Task Impressions
Dhrupad Bhardwaj, Julia Kempe, Artem Vysogorets +2
This work proposes a new method to sequentially train deep neural networks on multiple tasks without suffering catastrophic forgetting, while endowing it with the capability to qui…
Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity
Artem Vysogorets, Julia Kempe
Neural network pruning is a fruitful area of research with surging interest in high sparsity regimes. Benchmarking in this domain heavily relies on faithful representation of the s…