This work addresses the problem of optimally solving Markov Random Fields(MRFs) in which labels obey a certain topology constraint. Utilizing prior information, such as domain knowledge about the appearance, shape, or spatial configuration of objects in a scene can greatly improve the accuracy of segmentation algorithms in the presence of noise, clutter, and occlusion. Nowhere is this more evid...

The typical way for relation extraction is fine-tuning large pre-trained language models on task-specific datasets, then selecting the label with highest probability of output distribution as final prediction. However, usage Top-k prediction set a given sample commonly overlooked. In this paper, we first reveal that contains useful information predicting correct label. To effectively utilizes s...

Many problems in computer science can be represented by a graph and reducedto a graph clustering or k-way partitioning problem. In the classical definition,a graph consists of nodes and edges which usually connect exactly two nodes.Hypergraphs are a generalization of graphs, where every edge can connect anarbitrary number of nodes. Recent results suggest that some problems i...

Graph classification is an important data mining task, and various graph kernel methods have been proposed recently for this task. These methods have proven to be effective, but they tend to have high computational overhead. In this paper, we propose an alternative approach to graph classification that is based on feature-vectors constructed from different global topological attributes, as well...

Abstract Nowadays, the coupling of electronic structure and machine learning techniques serves as a powerful tool to predict chemical physical properties broad range systems. With aim improving accuracy predictions, large number representations for molecules solids applications has been developed. In this work we propose novel descriptor based on notion molecular graph. While graphs are largely...

1. Nonparametric Label Propagation (LP) has been proven to be effective for semi-supervised learning problems, and it predicts the labels for unlabeled data by a harmonic solution of an energy minimization problem which encourages local smoothness of the labels in accordance with the similarity graph. 2. On the other hand, the success of LP algorithms highly depends on the underlying similarity...

0:-logP(y i = 0 ; data)! 1:-logP(y i = 1 ; data) ! ∑ ∑ ∈ + = edges j i j i i i data y y data y data Energy , 2 1 θ ψ θ ψ θ y D.#Hoiem# Main idea: ! • Construct a graph such that every st-cut corresponds to a joint assignment to the variables y " ! • The cost of the cut should be equal to the energy of the assignment, E(y; data). " ! • The minimum-cut then corresponds to the minimum energy assig...

Visual data such as images and videos contain a rich source of structured semantic labels as well as a wide range of interacting components. Visual content could be assigned with fine-grained labels describing major components, coarse-grained labels depicting high level abstractions, or a set of labels revealing attributes. Such categorization over different, interacting layers of labels evince...

A discussion of graph energy for the AIM Workshop (October 23–27, 2006): Spectra of Families of Matrices described by Graphs, Digraphs, and Sign Patterns.