Qualitative models are often more suitable than classical quantitative models in tasks such as Model-based Diagnosis (MBD), explaining system behavior, and designing novel devices from first principles. Monotonicity is an important feature to leverage when constructing qualitative models. Detecting monotonic pieces robustly and efficiently from sensor or simulation data remains an open problem. This paper presents scale-based monotonicity: the notion that monotonicity can be defined relative to a scale. Real-valued functions defined on a finite set of reals e.g. sensor data or simulation results, can be partitioned into quasimonotonic segments, i.e. segments monotonic with respect to a scale, in linear time. A novel segmentation algorithm i...
This dissertation studies the incorporation of monotonicity constraints as a type of domain knowledg...
International audienceWe consider the problem of estimating monotonicity properties of a scalar-valu...
Cette thèse se place dans le contexte de la fiabilité structurale associée à des modèles numériques ...
Qualitative models are often more suitable than classical quantitative models in tasks such as Model...
Abstract. It is a crucial task to build qualitative models of industrial applications for model-base...
Abstract. It is a crucial task to build qualitative models of industrial applications for model-base...
Monotonicity is a simple yet significant quali-tative characteristic. We consider the problem of seg...
Chains are vector-valued signals sampling a curve. They are important to motion signal processing an...
In many problems in science and engineering ranging from astrophysics to geosciences to financial an...
textabstractThe monotonicity constraint is a common side condition imposed on modeling problems as d...
This paper investigates the relationship between approximation error and complexity. A variety of co...
Monotonicity is a constraint which arises in many application domains. We present a machine learning...
Real-world agents must react to changing conditions as they execute planned tasks. Conditions are ty...
AbstractThis paper investigates the relationship between approximation error and complexity. A varie...
textabstractThe monotonicity property is ubiquitous in our lives and it appears in different roles: ...
This dissertation studies the incorporation of monotonicity constraints as a type of domain knowledg...
International audienceWe consider the problem of estimating monotonicity properties of a scalar-valu...
Cette thèse se place dans le contexte de la fiabilité structurale associée à des modèles numériques ...
Qualitative models are often more suitable than classical quantitative models in tasks such as Model...
Abstract. It is a crucial task to build qualitative models of industrial applications for model-base...
Abstract. It is a crucial task to build qualitative models of industrial applications for model-base...
Monotonicity is a simple yet significant quali-tative characteristic. We consider the problem of seg...
Chains are vector-valued signals sampling a curve. They are important to motion signal processing an...
In many problems in science and engineering ranging from astrophysics to geosciences to financial an...
textabstractThe monotonicity constraint is a common side condition imposed on modeling problems as d...
This paper investigates the relationship between approximation error and complexity. A variety of co...
Monotonicity is a constraint which arises in many application domains. We present a machine learning...
Real-world agents must react to changing conditions as they execute planned tasks. Conditions are ty...
AbstractThis paper investigates the relationship between approximation error and complexity. A varie...
textabstractThe monotonicity property is ubiquitous in our lives and it appears in different roles: ...
This dissertation studies the incorporation of monotonicity constraints as a type of domain knowledg...
International audienceWe consider the problem of estimating monotonicity properties of a scalar-valu...
Cette thèse se place dans le contexte de la fiabilité structurale associée à des modèles numériques ...