Search Results for author:
Found 10 papers, 5 papers with code
DynaConF: Dynamic Forecasting of Non-Stationary Time Series
Deep learning has shown impressive results in a variety of time series forecasting tasks, where modeling the conditional distribution of the future given the past is the essence.
Efficient CDF Approximations for Normalizing Flows
Instead, we build upon the diffeomorphic properties of normalizing flows and leverage the divergence theorem to estimate the CDF over a closed region in target space in terms of the flux across its \emph{boundary}, as induced by the normalizing flow.
Agent Forecasting at Flexible Horizons using ODE Flows
In this work we describe OMEN, a neural ODE based normalizing flow for the prediction of marginal distributions at flexible evaluation horizons, and apply it to agent position forecasting.
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation.
Generating Videos of Zero-Shot Compositions of Actions and Objects
Human activity videos involve rich, varied interactions between people and objects.
Neural Volumes: Learning Dynamic Renderable Volumes from Images
Modeling and rendering of dynamic scenes is challenging, as natural scenes often contain complex phenomena such as thin structures, evolving topology, translucency, scattering, occlusion, and biological motion.
Variational Autoencoders with Jointly Optimized Latent Dependency Structure
In particular, we express the latent variable space of a variational autoencoder (VAE) in terms of a Bayesian network with a learned, flexible dependency structure.
Probabilistic Video Generation using Holistic Attribute Control
Videos express highly structured spatio-temporal patterns of visual data.
Non-parametric Structured Output Networks
End-to-end training methods for models with structured graphical dependencies on top of neural predictions have recently emerged as a principled way of combining these two paradigms.
Visual Reference Resolution using Attention Memory for Visual Dialog
From this memory, the model retrieves the previous attention, taking into account recency, which is most relevant for the current question, in order to resolve potentially ambiguous references.
Ranked #13 on
Visual Dialog
on VisDial v0.9 val
(R@1 metric)
