24 citations · 40 across the 6 of their papers we have counts for
6 papers
From Neurons to Neutrons: A Case Study in Interpretability
Ouail Kitouni, Niklas Nolte, Víctor Samuel Pérez-Díaz +2
Mechanistic Interpretability (MI) promises a path toward fully understanding how neural networks make their predictions. Prior work demonstrates that even when trained to perform s…
NuCLR: Nuclear Co-Learned Representations
Ouail Kitouni, Niklas Nolte, Sokratis Trifinopoulos +2
We introduce Nuclear Co-Learned Representations (NuCLR), a deep learning model that predicts various nuclear observables, including binding and decay energies, and nuclear charge r…
Expressive Monotonic Neural Networks
Ouail Kitouni, Niklas Nolte, Michael Williams
The monotonic dependence of the outputs of a neural network on some of its inputs is a crucial inductive bias in many scenarios where domain knowledge dictates such behavior. This…
Finding NEEMo: Geometric Fitting using Neural Estimation of the Energy Mover's Distance
Ouail Kitouni, Niklas Nolte, Mike Williams
A novel neural architecture was recently developed that enforces an exact upper bound on the Lipschitz constant of the model by constraining the norm of its weights in a minimal wa…
Towards Understanding Grokking: An Effective Theory of Representation Learning
Ziming Liu, Ouail Kitouni, Niklas Nolte +3
We aim to understand grokking, a phenomenon where models generalize long after overfitting their training set. We present both a microscopic analysis anchored by an effective theor…
Robust and Provably Monotonic Networks
Ouail Kitouni, Niklas Nolte, Mike Williams
The Lipschitz constant of the map between the input and output space represented by a neural network is a natural metric for assessing the robustness of the model. We present a new…