Search Results for author:
Found 11 papers, 7 papers with code
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data.
Improving new physics searches with diffusion models for event observables and jet constituents
We introduce a new technique called Drapes to enhance the sensitivity in searches for new physics at the LHC.
Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation
We propose a protocol called flows for flows for training normalizing flows to morph one dataset into another even if the underlying probability density of neither dataset is known explicitly.
Multimodal Neurons in Pretrained Text-Only Transformers
Language models demonstrate remarkable capacity to generalize representations learned in one modality to downstream tasks in other modalities.
CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation
Model independent techniques for constructing background data templates using generative models have shown great promise for use in searches for new physics processes at the LHC.
Decorrelation with conditional normalizing flows
The sensitivity of many physics analyses can be enhanced by constructing discriminants that preferentially select signal events.
Flows for Flows: Training Normalizing Flows Between Arbitrary Distributions with Maximum Likelihood Estimation
Normalizing flows are constructed from a base distribution with a known density and a diffeomorphism with a tractable Jacobian.
Flowification: Everything is a Normalizing Flow
On the other hand, neural networks only perform a forward pass on the input, there is neither a notion of an inverse of a neural network nor is there one of its likelihood contribution.
Funnels: Exact maximum likelihood with dimensionality reduction
Normalizing flows are diffeomorphic, typically dimension-preserving, models trained using the likelihood of the model.
Toward a Visual Concept Vocabulary for GAN Latent Space
A large body of recent work has identified transformations in the latent spaces of generative adversarial networks (GANs) that consistently and interpretably transform generated images.
Condition Monitoring of Drive Trains by Data Fusion of Acoustic Emission and Vibration Sensors
Early damage detection and classification by condition monitoring systems is crucial to enable predictive maintenance of manufacturing systems and industrial facilities.
