Unsupervised Learning

We study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can …

Conference on Neural Information Processing Systems (NeurIPS), 2019.

Compositional Pattern Producing Networks (CPPNs) are differentiable networks that independently map (x, y) pixel coordinates to (r, g, …

Workshop at the International Conference on Computer Vision (ICCV), 2019.

Computing optimal transport maps between high-dimensional and continuous distributions is a challenging problem in optimal transport …

ArXiv, 2019.

Importance weighted variational inference (Burda et al., 2015) uses multiple i.i.d. samples to have a tighter variational lower bound. …

International Conference on Machine Learning (ICML), 2019.

In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We …

Workshop at the International Conference on Learning Representations (ICLR), 2019.

We introduce a new dataset of 293,008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by …

Women in Machine Learning (WiML), 2018.

We present an unsupervised approach for learning to estimate three dimensional (3D) facial structure from a single image while also …

Conference on Neural Information Processing Systems (NeurIPS), 2018.

We introduce a new dataset of 293,008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by …

Workshop at the International Conference on Machine Learning (ICML), 2018.

Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, …

International Conference on Machine Learning (ICML), 2018.

The objective of unsupervised domain adaptation is to leverage features from a labeled source domain and learn a classifier for an …

Pedro O. Pinheiro

Computer Vision and Pattern Recognition (CVPR), 2018.