Search Results for author:
Found 19 papers, 2 papers with code
EventSleep: Sleep Activity Recognition with Event Cameras
Our novel pipeline is able to achieve high accuracy under these challenging conditions and incorporates a Bayesian approach (Laplace ensembles) to increase the robustness in the predictions, which is fundamental for medical applications.
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation
Model-based RL, by building a dynamic model of the robot, enables data reuse and transfer learning between tasks with the same robot and similar environment.
Gaussian Mixture Models for Affordance Learning using Bayesian Networks
Affordances are fundamental descriptors of relationships between actions, objects and effects.
Multi-label affordance mapping from egocentric vision
We use this method to build the largest and most complete dataset on affordances based on the EPIC-Kitchen dataset, EPIC-Aff, which provides interaction-grounded, multi-label, metric and spatial affordance annotations.
LightDepth: Single-View Depth Self-Supervision from Illumination Decline
However, there are scenarios, especially in medicine in the case of endoscopies, where such data cannot be obtained.
Bayesian Deep Learning for Affordance Segmentation in images
We present a novel Bayesian deep network to detect affordances in images, at the same time that we quantify the distribution of the aleatoric and epistemic variance at the spatial level.
Assessing visual acuity in visual prostheses through a virtual-reality system
The development of new artificial vision simulation systems can be useful to guide the development of new visual devices and the optimization of field of view and resolution to provide a helpful and valuable visual aid to profoundly or totally blind patients.
Augmented reality navigation system for visual prosthesis
In this work, we propose an augmented reality navigation system for visual prosthesis that incorporates a software of reactive navigation and path planning which guides the subject through convenient, obstacle-free route.
Bayesian deep learning of affordances from RGB images
Our Bayesian model is able to capture both the aleatoric uncertainty from the scene and the epistemic uncertainty associated with the model and previous learning process.
Robust Policy Search for Robot Navigation with Stochastic Meta-Policies
First, to deal with input noise and provide a safe and repeatable policy we use an improved version of unscented Bayesian optimization.
Fully Distributed Bayesian Optimization with Stochastic Policies
But, when compared with other acquisition functions in the sequential setting, Thompson sampling is known to perform suboptimally.
Structural and object detection for phosphene images
Most research in simulated prosthetic vision is performed based on static images, while very few researchers have addressed the problem of scene recognition through video sequences.
Practical Bayesian optimization in the presence of outliers
In this paper, we present an empirical evaluation of Bayesian optimization methods in the presence of outliers.
Robust Bayesian Optimization with Student-t Likelihood
Bayesian optimization has recently attracted the attention of the automatic machine learning community for its excellent results in hyperparameter tuning.
Funneled Bayesian Optimization for Design, Tuning and Control of Autonomous Systems
In order to generalize to unknown functions in a black-box fashion, the common assumption is that the underlying function can be modeled with a stationary process.
Unscented Bayesian Optimization for Safe Robot Grasping
We address the robot grasp optimization problem of unknown objects considering uncertainty in the input space.
Local Nonstationarity for Efficient Bayesian Optimization
Bayesian optimization has shown to be a fundamental global optimization algorithm in many applications: ranging from automatic machine learning, robotics, reinforcement learning, experimental design, simulations, etc.
BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits
BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems.
BayesOpt: A Library for Bayesian optimization with Robotics Applications
On one side, we present a general framework for Bayesian optimization and we compare it with some related fields in active learning and Bayesian numerical analysis.
