Graph neural networksLoopy belief propagationMarkov networksHow can we classify graph-structured data only with positive labels? Graph-based positive-unlabeled (PU) learning is to train a binary classifier given only the positive labels when the relationship between examples is given as a graph. The...
A basin class is a set of neural networks that have the same basins. The attractors in those basins and the paths to those attractors, however, may be different. The networks that produce the NT-graph in Figure 18 are in the same basin class as those for Figure 19, as the basins are...
IMDB-BINARY is a movie collaboration dataset that consists of the ego-networks of 1,000 actors/actresses who played roles in movies in IMDB. In each graph, nodes represent actors/actress, and there is an edge between them if they appear in the same movie
directed graphinformation theoryredundancysequential memorysparse codingsparse coding.An extension to a recently introduced architecture of clique-based neural networks is presented. This extension makes it possible to store sequences with high efficiency. To obtain this property, network connections are ...
we firstly analyze the features of Hamming sphere dimple with Hamming-graph,and then propose an algorithm for judging whether a boolean function is linearly or nonlinearly separable Hamming sphere dimple by sorting the weighted height of the true nodes.Furthermore,we decompose Hamming sphere dimple ...
In this pa- per we propose semantic-aware neural networks to extract the semantic information of the binary code. Specially, we use BERT to pre-train the binary code on one token-level task, one block-level task, and two graph-level tasks. Moreover, we find that the order of the CFG...
Dong and Yang are now looking to apply a GAN that adopts binary neurons to the realization of a conditional computation graph. In this instance, some components would be activated and deactivated, according to the decisions made by the network's binaryneurons. ...
论文阅读:Order Matters: Semantic-Aware Neural Networks for Binary Code Similarity Detection 摘要: 二进制代码相似性检测:检测相似二进制函数 传统方法:graph matching algorithms 二进制函数通常以CFG的形式表示出来,同时有manually selected block features
Discrete graph hashing. In Proc. Advances in Neural Information Processing Systems (NIPS), 2014. 26. Shai Shalev-Shwartz, Alon Gonen, and Ohad Shamir. Large-scale convex min- imization with a low-rank constraint. In Proc. International Conf. on Machine Learning (ICML), 2011. 27. Xinhua ...
Graph neural networks A binary function can be represented by a directedCFG = (V, E)equivalently, whereVrepresents a set of vertices of basic blocks in the CFG, and edge(u,v)\(\in \)Edenotes theTRUE-FALSErelationship between vertexuand vertexv. We do not distinguish such types of edges...