Search Results for author:
Found 15 papers, 9 papers with code
Why does Throwing Away Data Improve Worst-Group Error?
When facing data with imbalanced classes or groups, practitioners follow an intriguing strategy to achieve best results.
Simple data balancing achieves competitive worst-group-accuracy
We study the problem of learning classifiers that perform well across (known or unknown) groups of data.
Ranked #1 on
Out-of-Distribution Generalization
on UrbanCars
Out of Distribution Generalization in Machine Learning
A central topic in the thesis is the strong link between discovering the causal structure of the data, finding features that are reliable (when using them to predict) regardless of their context, and out of distribution generalization.
BIG-bench Machine Learning
Out-of-Distribution Generalization
Linear unit-tests for invariance discovery
There is an increasing interest in algorithms to learn invariant correlations across training environments.
Low Distortion Block-Resampling with Spatially Stochastic Networks
We formalize and attack the problem of generating new images from old ones that are as diverse as possible, only allowing them to change without restrictions in certain parts of the image while remaining globally consistent.
Symplectic Recurrent Neural Networks
We propose Symplectic Recurrent Neural Networks (SRNNs) as learning algorithms that capture the dynamics of physical systems from observed trajectories.
Invariant Risk Minimization
We introduce Invariant Risk Minimization (IRM), a learning paradigm to estimate invariant correlations across multiple training distributions.
Geometrical Insights for Implicit Generative Modeling
Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion.
Wasserstein Generative Adversarial Networks
We introduce a new algorithm named WGAN, an alternative to traditional GAN training.
Improved Training of Wasserstein GANs
Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability.
Ranked #3 on
Image Generation
on CAT 256x256
Wasserstein GAN
We introduce a new algorithm named WGAN, an alternative to traditional GAN training.
Towards Principled Methods for Training Generative Adversarial Networks
The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks.
Adversarially Learned Inference
We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process.
Unitary Evolution Recurrent Neural Networks
When the eigenvalues of the hidden to hidden weight matrix deviate from absolute value 1, optimization becomes difficult due to the well studied issue of vanishing and exploding gradients, especially when trying to learn long-term dependencies.
Ranked #26 on
Sequential Image Classification
on Sequential MNIST
Saddle-free Hessian-free Optimization
Nonconvex optimization problems such as the ones in training deep neural networks suffer from a phenomenon called saddle point proliferation.
