bootstrap
bootstrap-latent-thought-dataThis dataset is associated with the paper Reasoning to Learn from Latent Thoughts. It contains data used for pretraining language models with a focus on improving data efficiency by modeling and inferring latent thoughts underlying the text generation process, such as on reasoning-intensive math corpus. An expectation-maximization algorithm is developed for models to self-improve their self-generated thoughts and data efficiency.
pod-bootstrapgraphical-bootstrap-correlator-dataset
Graphical Bootstrap Correlator Dataset
This dataset contains large-scale graph-structured data arising from high-order perturbative computations of four-point correlators in planar $\mathcal{N}=4$ super Yang--Mills theory.
The data consists of denominator graphs (d-graphs) appearing in the graphical bootstrap formulation of correlators. Each graph is associated with a binary label indicating whether it contributes to the correlator at a given perturbative order.
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/Gabriele-dian/graphical-bootstrap-correlator-dataset.graphical-bootstrap-correlator-dataset
Graphical Bootstrap Correlator Dataset
This dataset contains large-scale graph-structured data arising from high-order perturbative computations of four-point correlators in planar $\mathcal{N}=4$ super Yang--Mills theory.
The data consists of denominator graphs (d-graphs) appearing in the graphical bootstrap formulation of correlators. Each graph is associated with a binary label indicating whether it contributes to the correlator at a given perturbative order.
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/anonymous314/graphical-bootstrap-correlator-dataset.synth-bootstrap-trialguile-bootstrap