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Williams Paquier

dblp:61/2296 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2005
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Image recognition and object detection · 72% Robot manipulation · 28%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection › visual recognition
scene and object recognition		 0.112005
Combining Structural Descriptions and Image-based Representations for Image, Object, and Scene Recognition · IJCAI 2005
Computer vision › Image recognition and object detection
scene recognition		 0.112005
Combining Structural Descriptions and Image-based Representations for Image, Object, and Scene Recognition · IJCAI 2005

Methods — techniques the papers use, named apart from their topics

structural descriptions · 0.1image-based representations · 0.1sensory-motor learning · 0.0reinforcement learning · 0.0
YearPublicationVenuePosition
2005 Combining Structural Descriptions and Image-based Representations for Image, Object, and Scene Recognition
Nicolas Do Huu, Williams Paquier, Raja Chatila 0001
 IJCAI2
2003 Learning new representations and goals for autonomous robots
abstract
Most robotic systems are designed for given goals. Even learning systems follow this paradigm by trying to improve overall performance for a given task. These systems are limited by the knowledge of the developers and are not able to overpass their initial set of goals. We propose to explore a new kind of sensory motor systems that are able to acquire new representations and new goals starting from an initial small set. Instead of developing algorithms for a given task, we want to develop a general approach that is task acquisition oriented. The work reported in this paper is just a beginning and propose a theoretical framework and first results for such systems.
Williams Paquier, Raja Chatila 0001
 ICRA1