Search Results for author: Luis Pineda

Found 12 papers, 8 papers with code

On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction

1 code implementation30 Mar 2022 Tim Bakker, Matthew Muckley, Adriana Romero-Soriano, Michal Drozdzal, Luis Pineda

Most current approaches to undersampled multi-coil MRI reconstruction focus on learning the reconstruction model for a fixed, equidistant acquisition trajectory.

MRI Reconstruction SSIM

K-level Reasoning for Zero-Shot Coordination in Hanabi

no code implementations NeurIPS 2021 Brandon Cui, Hengyuan Hu, Luis Pineda, Jakob Foerster

The standard problem setting in cooperative multi-agent settings is \emph{self-play} (SP), where the goal is to train a \emph{team} of agents that works well together.

Active 3D Shape Reconstruction from Vision and Touch

1 code implementation NeurIPS 2021 Edward J. Smith, David Meger, Luis Pineda, Roberto Calandra, Jitendra Malik, Adriana Romero, Michal Drozdzal

In this paper, we focus on this problem and introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2)a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile signals; and 3) a set of data-driven solutions with either tactile or visuotactile priors to guide the shape exploration.

3D Reconstruction 3D Shape Reconstruction

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning

3 code implementations20 Apr 2021 Luis Pineda, Brandon Amos, Amy Zhang, Nathan O. Lambert, Roberto Calandra

MBRL-Lib is designed as a platform for both researchers, to easily develop, debug and compare new algorithms, and non-expert user, to lower the entry-bar of deploying state-of-the-art algorithms.

Model-based Reinforcement Learning reinforcement-learning

Off-Belief Learning

2 code implementations6 Mar 2021 Hengyuan Hu, Adam Lerer, Brandon Cui, David Wu, Luis Pineda, Noam Brown, Jakob Foerster

Policies learned through self-play may adopt arbitrary conventions and implicitly rely on multi-step reasoning based on fragile assumptions about other agents' actions and thus fail when paired with humans or independently trained agents at test time.

On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning

1 code implementation26 Feb 2021 Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra

We demonstrate that this problem can be tackled effectively with automated HPO, which we demonstrate to yield significantly improved performance compared to human experts.

Hyperparameter Optimization Model-based Reinforcement Learning +1

Active MR k-space Sampling with Reinforcement Learning

2 code implementations20 Jul 2020 Luis Pineda, Sumana Basu, Adriana Romero, Roberto Calandra, Michal Drozdzal

Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition.

Image Reconstruction reinforcement-learning

On the Evaluation of Conditional GANs

1 code implementation11 Jul 2019 Terrance DeVries, Adriana Romero, Luis Pineda, Graham W. Taylor, Michal Drozdzal

We show that FJD can be used as a promising single metric for cGAN benchmarking and model selection.

Model Selection

Learning Causal State Representations of Partially Observable Environments

no code implementations25 Jun 2019 Amy Zhang, Zachary C. Lipton, Luis Pineda, Kamyar Azizzadenesheli, Anima Anandkumar, Laurent Itti, Joelle Pineau, Tommaso Furlanello

In this paper, we propose an algorithm to approximate causal states, which are the coarsest partition of the joint history of actions and observations in partially-observable Markov decision processes (POMDP).

Causal Inference

Elucidating image-to-set prediction: An analysis of models, losses and datasets

1 code implementation11 Apr 2019 Luis Pineda, Amaia Salvador, Michal Drozdzal, Adriana Romero

In this paper, we identify an important reproducibility challenge in the image-to-set prediction literature that impedes proper comparisons among published methods, namely, researchers use different evaluation protocols to assess their contributions.

Multi-Label Classification

Planning in Stochastic Environments with Goal Uncertainty

no code implementations18 Oct 2018 Sandhya Saisubramanian, Kyle Hollins Wray, Luis Pineda, Shlomo Zilberstein

The framework extends the stochastic shortest path (SSP) model to dynamic environments in which it is impossible to determine the exact goal states ahead of plan execution.

Decision Making

Generalizing the Role of Determinization in Probabilistic Planning

no code implementations21 May 2017 Luis Pineda, Shlomo Zilberstein

The stochastic shortest path problem (SSP) is a highly expressive model for probabilistic planning.

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