Search Results for author:
Found 14 papers, 3 papers with code
Towards a Unified Framework for Sequential Decision Making
In recent years, the integration of Automated Planning (AP) and Reinforcement Learning (RL) has seen a surge of interest.
On Using Admissible Bounds for Learning Forward Search Heuristics
This results in a different loss function from the MSE commonly employed in the literature, which implicitly models the learned heuristic as a gaussian distribution.
A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making
Conversely, Reinforcement Learning (RL) proposes to learn the solution of the SDP from data, without a world model, and represent the learned knowledge subsymbolically.
A Survey on Semi-Supervised Semantic Segmentation
Semantic segmentation is one of the most challenging tasks in computer vision.
NeSIG: A Neuro-Symbolic Method for Learning to Generate Planning Problems
In this paper we propose NeSIG, to the best of our knowledge the first domain-independent method for automatically generating planning problems that are valid, diverse and difficult to solve.
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis.
Custom Structure Preservation in Face Aging
In this work, we propose a novel architecture for face age editing that can produce structural modifications while maintaining relevant details present in the original image.
Learning Visual Voice Activity Detection with an Automatically Annotated Dataset
Visual voice activity detection (V-VAD) uses visual features to predict whether a person is speaking or not.
Extended Gaze Following: Detecting Objects in Videos Beyond the Camera Field of View
In this paper we address the problems of detecting objects of interest in a video and of estimating their locations, solely from the gaze directions of people present in the video.
Understanding Priors in Bayesian Neural Networks at the Unit Level
We investigate deep Bayesian neural networks with Gaussian weight priors and a class of ReLU-like nonlinearities.
DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model
In this paper, we address the problem of how to robustly train a ConvNet for regression, or deep robust regression.
A Comprehensive Analysis of Deep Regression
Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks.
Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction
Our approach enables a robot to learn and to adapt its gaze control strategy for human-robot interaction neither with the use of external sensors nor with human supervision.
Deep Mixture of Linear Inverse Regressions Applied to Head-Pose Estimation
In this particular problem, we show that inverse regression outperforms regression models currently used by state-of-the-art computer vision methods.
