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Found 7 papers, 3 papers with code
DiSK: A Diffusion Model for Structured Knowledge
We introduce Diffusion Models of Structured Knowledge (DiSK) - a new architecture and training approach specialized for structured data.
Expressive Monotonic Neural Networks
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.
NuCLR: Nuclear Co-Learned Representations
We introduce Nuclear Co-Learned Representations (NuCLR), a deep learning model that predicts various nuclear observables, including binding and decay energies, and nuclear charge radii.
Finding NEEMo: Geometric Fitting using Neural Estimation of the Energy Mover's Distance
We present a new and interesting direction for this architecture: estimation of the Wasserstein metric (Earth Mover's Distance) in optimal transport by employing the Kantorovich-Rubinstein duality to enable its use in geometric fitting applications.
Towards Understanding Grokking: An Effective Theory of Representation Learning
We aim to understand grokking, a phenomenon where models generalize long after overfitting their training set.
Robust and Provably Monotonic Networks
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.
Enhancing searches for resonances with machine learning and moment decomposition
A key challenge in searches for resonant new physics is that classifiers trained to enhance potential signals must not induce localized structures.
High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability
