Search Results for author:
Found 7 papers, 4 papers with code
Training neural operators to preserve invariant measures of chaotic attractors
In this paper, we propose an alternative framework designed to preserve invariant measures of chaotic attractors that characterize the time-invariant statistical properties of the dynamics.
Deep Stochastic Mechanics
This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schr\"odinger equation inspired by stochastic mechanics and generative diffusion models.
Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting
This study explores an application of machine learning (ML) models as post-processing tools for subseasonal forecasting.
Comparing the costs of abstraction for DL frameworks
High level abstractions for implementing, training, and testing Deep Learning (DL) models abound.
Tensorized Embedding Layers
The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing.
Tensorized Embedding Layers for Efficient Model Compression
The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing.
Generative Models for Fast Calorimeter Simulation.LHCb case
Simulation is one of the key components in high energy physics.
