Search Results for author:
Found 12 papers, 9 papers with code
ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables
Covariates provide valuable information on external factors that influence time series and are critical in many real-world time series forecasting tasks.
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization
To address this question, we develop WaveToken, a wavelet-based tokenizer that allows models to learn complex representations directly in the space of time-localized frequencies.
Hard Constraint Guided Flow Matching for Gradient-Free Generation of PDE Solutions
Generative models that satisfy hard constraints are crucial in many scientific and engineering applications where physical laws or system requirements must be strictly respected.
Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics
Remarkable progress in the development of Deep Learning Weather Prediction (DLWP) models positions them to become competitive with traditional numerical weather prediction (NWP) models.
Chronos: Learning the Language of Time Series
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models.
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting
Prior works on time series diffusion models have primarily focused on developing conditional models tailored to specific forecasting or imputation tasks.
Generative Modeling with Flow-Guided Density Ratio Learning
We present Flow-Guided Density Ratio Learning (FDRL), a simple and scalable approach to generative modeling which builds on the stale (time-independent) approximation of the gradient flow of entropy-regularized f-divergences introduced in recent work.
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series
Learning accurate predictive models of real-world dynamic phenomena (e. g., climate, biological) remains a challenging task.
Deep Explicit Duration Switching Models for Time Series
We propose the Recurrent Explicit Duration Switching Dynamical System (RED-SDS), a flexible model that is capable of identifying both state- and time-dependent switching dynamics.
Refining Deep Generative Models via Discriminator Gradient Flow
We introduce Discriminator Gradient flow (DGflow), a new technique that improves generated samples via the gradient flow of entropy-regularized f-divergences between the real and the generated data distributions.
Ranked #1 on
Text Generation
on One Billion Word
A Characteristic Function Approach to Deep Implicit Generative Modeling
In this paper, we formulate the problem of learning an IGM as minimizing the expected distance between characteristic functions.
Hyperprior Induced Unsupervised Disentanglement of Latent Representations
We address the problem of unsupervised disentanglement of latent representations learnt via deep generative models.
