Add to ORKGWe propose novel methods for machine learning of structured output spaces. Specifically, we consider outputs which are graphs with vertices that have a natural order. We consider the usual adjacency matrix representation of graphs, as well as two other representations for such a graph: (a) decomposing the graph into a set of paths, (b) converting the graph into a single sequence of nodes with labeled edges. For each of the three representations, we propose an encoding and decoding scheme. We also propose an evaluation measure for comparing two graphs
This paper presents an approach to the classification of structured data with graphs. We suggest to ...
In this paper, we describe the use of concepts from structural and statistical pattern recognition f...
Graph-structured data appears frequently in domains including chemistry, natural language semantics,...
We propose novel methods for machine learning of structured output spaces. Specifically, we consider...
Abstract. We propose novel methods for machine learning of structured output spaces. Specifically, w...
This paper introduces a novel approach for processing a general class of structured information, viz...
This paper introduces a novel approach for processing a general class of structured information, viz...
A structured organization of information is typically required by symbolic processing. On the other ...
Undirected graphs are frequently used to model phenomena that deal with interacting objects, such as...
In this paper, we propose a novel model for learning graph representations, which generates a low-di...
Depending on the node ordering, an adjacency matrix can highlight distinct characteristics of a grap...
How to represent a graph in memory is a fundamental data-structuring problem. In the usual represent...
Recent decades have witnessed the prosperity of deep learning which has revolutionized a broad varie...
The construction of a meaningful graph topology plays a crucial role in the effective representation...
The construction of a meaningful graph topology plays a crucial role in the effective representation...
This paper presents an approach to the classification of structured data with graphs. We suggest to ...
In this paper, we describe the use of concepts from structural and statistical pattern recognition f...
Graph-structured data appears frequently in domains including chemistry, natural language semantics,...
We propose novel methods for machine learning of structured output spaces. Specifically, we consider...
Abstract. We propose novel methods for machine learning of structured output spaces. Specifically, w...
This paper introduces a novel approach for processing a general class of structured information, viz...
This paper introduces a novel approach for processing a general class of structured information, viz...
A structured organization of information is typically required by symbolic processing. On the other ...
Undirected graphs are frequently used to model phenomena that deal with interacting objects, such as...
In this paper, we propose a novel model for learning graph representations, which generates a low-di...
Depending on the node ordering, an adjacency matrix can highlight distinct characteristics of a grap...
How to represent a graph in memory is a fundamental data-structuring problem. In the usual represent...
Recent decades have witnessed the prosperity of deep learning which has revolutionized a broad varie...
The construction of a meaningful graph topology plays a crucial role in the effective representation...
The construction of a meaningful graph topology plays a crucial role in the effective representation...
This paper presents an approach to the classification of structured data with graphs. We suggest to ...
In this paper, we describe the use of concepts from structural and statistical pattern recognition f...
Graph-structured data appears frequently in domains including chemistry, natural language semantics,...