Search Results for author:
Found 10 papers, 6 papers with code
Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous Scenes
We present a method enabling the scaling of NeRFs to learn a large number of semantically-similar scenes.
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance for Non-Parametric Generative Modeling
Safeguarding privacy in sensitive training data is paramount, particularly in the context of generative modeling.
RefinedFields: Radiance Fields Refinement for Unconstrained Scenes
Modeling large scenes from unconstrained images has proven to be a major challenge in computer vision.
Unifying GANs and Score-Based Diffusion as Generative Particle Models
Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance.
Continuous PDE Dynamics Forecasting with Implicit Neural Representations
Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations.
A Neural Tangent Kernel Perspective of GANs
We propose a novel theoretical framework of analysis for Generative Adversarial Networks (GANs).
PDE-Driven Spatiotemporal Disentanglement
A recent line of work in the machine learning community addresses the problem of predicting high-dimensional spatiotemporal phenomena by leveraging specific tools from the differential equations theory.
Stochastic Latent Residual Video Prediction
Designing video prediction models that account for the inherent uncertainty of the future is challenging.
Ranked #1 on
Video Prediction
on Cityscapes 128x128
(Pred metric)
Unsupervised Scalable Representation Learning for Multivariate Time Series
Time series constitute a challenging data type for machine learning algorithms, due to their highly variable lengths and sparse labeling in practice.
Robustness of classifiers to uniform $\ell\_p$ and Gaussian noise
We study the robustness of classifiers to various kinds of random noise models.
