38 citations · 112 across the 8 of their papers we have counts for
9 papers
AU-NN: ANFIS Unit Neural Network
Tonatiuh Hernández-del-Toro, Carlos A. Reyes-García, Luis Villaseñor-Pineda
In this paper is described the ANFIS Unit Neural Network, a deep neural network where each neuron is an independent ANFIS. Two use cases of this network are shown to test the capab…
On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction
Tim Bakker, Matthew Muckley, Adriana Romero-Soriano +2
Most current approaches to undersampled multi-coil MRI reconstruction focus on learning the reconstruction model for a fixed, equidistant acquisition trajectory. In this paper, we…
Active 3D Shape Reconstruction from Vision and Touch
Edward J. Smith, David Meger, Luis Pineda +4
Humans build 3D understandings of the world through active object exploration, using jointly their senses of vision and touch. However, in 3D shape reconstruction, most recent prog…
Active MR k-space Sampling with Reinforcement Learning
Luis Pineda, Sumana Basu, Adriana Romero +2
Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition. The majority of existing work have focused on designing bette…
On the Evaluation of Conditional GANs
Terrance DeVries, Adriana Romero, Luis Pineda +2
Conditional Generative Adversarial Networks (cGANs) are finding increasingly widespread use in many application domains. Despite outstanding progress, quantitative evaluation of su…
Learning Causal State Representations of Partially Observable Environments
Amy Zhang, Zachary C. Lipton, Luis Pineda +5
Intelligent agents can cope with sensory-rich environments by learning task-agnostic state abstractions. In this paper, we propose an algorithm to approximate causal states, which…
Elucidating image-to-set prediction: An analysis of models, losses and datasets
Luis Pineda, Amaia Salvador, Michal Drozdzal +1
In this paper, we identify an important reproducibility challenge in the image-to-set prediction literature that impedes proper comparisons among published methods, namely, researc…
Planning in Stochastic Environments with Goal Uncertainty
Sandhya Saisubramanian, Kyle Hollins Wray, Luis Pineda +1
We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem -- a general framework to model path planning and decision making in stochastic environments with goal uncert…
Generalizing the Role of Determinization in Probabilistic Planning
Luis Pineda, Shlomo Zilberstein
The stochastic shortest path problem (SSP) is a highly expressive model for probabilistic planning. The computational hardness of SSPs has sparked interest in determinization-based…