Add to ORKGObject recognition in the real world is a big challenge in the field of computer vision. Given the potentially enormous size of the search space it is essential to be able to make intelligent decisions about where in the visual field to obtain information from to reduce the computational resources needed. In this report a POMDP (Partially Observable Markov Decision Process) learning framework, using a policy gradient method and information rewards as a training signal, has been implemented and used to train fixation policies that aim to maximize the information gathered in each fixation. The purpose of such policies is to make object recognition faster by reducing the number of fixations needed. The trained policies are evaluated by simulat...
The problem of making optimal decisions in uncertain conditions is central to Artificial Intelligenc...
Publisher Copyright: IEEENoisy sensing, imperfect control, and environment changes are defining char...
We present a model-free reinforcement learning method for partially observable Markov decision probl...
Partially observable Markov decision processes (pomdp's) model decision problems in which an a...
Policy-gradient algorithms are attractive as a scalable approach to learning approximate policies fo...
Partially observable Markov decision processes (POMDPs) are interesting because they provide a gener...
Partially Observable Markov Decision Processes (POMDPs) provide a rich representation for agents act...
People are efficient when they make decisions under uncertainty, even when their decisions have long...
Colloque avec actes et comité de lecture. internationale.International audienceA new algorithm for s...
This ongoing phD work aims at proposing a unified framework to optimize both perception and task pla...
In active perception tasks, an agent aims to select sensory actions that reduce its uncertainty abou...
In active perception tasks, an agent aims to select sensory actions that reduce its uncertainty abou...
Sign language (SL) recognition modules in human-computer interaction systems need to be both fast an...
The first part of a two-part series of papers provides a survey on recent advances in Deep Reinforce...
In active perception tasks, an agent aims to select actions that reduce its uncertainty about a hidd...
The problem of making optimal decisions in uncertain conditions is central to Artificial Intelligenc...
Publisher Copyright: IEEENoisy sensing, imperfect control, and environment changes are defining char...
We present a model-free reinforcement learning method for partially observable Markov decision probl...
Partially observable Markov decision processes (pomdp's) model decision problems in which an a...
Policy-gradient algorithms are attractive as a scalable approach to learning approximate policies fo...
Partially observable Markov decision processes (POMDPs) are interesting because they provide a gener...
Partially Observable Markov Decision Processes (POMDPs) provide a rich representation for agents act...
People are efficient when they make decisions under uncertainty, even when their decisions have long...
Colloque avec actes et comité de lecture. internationale.International audienceA new algorithm for s...
This ongoing phD work aims at proposing a unified framework to optimize both perception and task pla...
In active perception tasks, an agent aims to select sensory actions that reduce its uncertainty abou...
In active perception tasks, an agent aims to select sensory actions that reduce its uncertainty abou...
Sign language (SL) recognition modules in human-computer interaction systems need to be both fast an...
The first part of a two-part series of papers provides a survey on recent advances in Deep Reinforce...
In active perception tasks, an agent aims to select actions that reduce its uncertainty about a hidd...
The problem of making optimal decisions in uncertain conditions is central to Artificial Intelligenc...
Publisher Copyright: IEEENoisy sensing, imperfect control, and environment changes are defining char...
We present a model-free reinforcement learning method for partially observable Markov decision probl...