Search Results for author:
Found 7 papers, 1 papers with code
Near Optimal Heteroscedastic Regression with Symbiotic Learning
Our work shows that we can estimate $\mathbf{w}^{*}$ in squared norm up to an error of $\tilde{O}\left(\|\mathbf{f}^{*}\|^2 \cdot \left(\frac{1}{n} + \left(\frac{d}{n}\right)^2\right)\right)$ and prove a matching lower bound (upto log factors).
Utilising the CLT Structure in Stochastic Gradient based Sampling : Improved Analysis and Faster Algorithms
We consider stochastic approximations of sampling algorithms, such as Stochastic Gradient Langevin Dynamics (SGLD) and the Random Batch Method (RBM) for Interacting Particle Dynamcs (IPD).
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization
We obtain tight convergence rates for RR and SO and demonstrate that these strategies lead to faster convergence than uniform sampling.
NeurInt : Learning to Interpolate through Neural ODEs
In this work, we propose a novel generative model that learns a flexible non-parametric prior over interpolation trajectories, conditioned on a pair of source and target images.
NeurInt-Learning Interpolation by Neural ODEs
A range of applications require learning image generation models whose latent space effectively captures the high-level factors of variation in the data distribution, which can be judged by its ability to interpolate between images smoothly.
Jointly Trained Image and Video Generation using Residual Vectors
The proposed model allows minor variations in content across frames while maintaining the temporal dependence through latent vectors encoding the pose or motion features.
TorchGAN: A Flexible Framework for GAN Training and Evaluation
The key features of TorchGAN are its extensibility, built-in support for a large number of popular models, losses and evaluation metrics, and zero overhead compared to vanilla PyTorch.
