1.4k citations · 6.3k across the 46 of their papers we have counts for
47 papers
Kernels, Data & Physics
Francesco Cagnetta, Deborah Oliveira, Mahalakshmi Sabanayagam +2
Lecture notes from the course given by Professor Julia Kempe at the summer school "Statistical physics of Machine Learning" in Les Houches. The notes discuss the so-called NTK appr…
Wavelets Beat Monkeys at Adversarial Robustness
Jingtong Su, Julia Kempe
Research on improving the robustness of neural networks to adversarial noise - imperceptible malicious perturbations of the data - has received significant attention. The currently…
What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?
Nikolaos Tsilivis, Julia Kempe
The adversarial vulnerability of neural nets, and subsequent techniques to create robust models have attracted significant attention; yet we still lack a full understanding of this…
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…
Can we achieve robustness from data alone?
Nikolaos Tsilivis, Jingtong Su, Julia Kempe
We introduce a meta-learning algorithm for adversarially robust classification. The proposed method tries to be as model agnostic as possible and optimizes a dataset prior to its d…
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…