Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Bassan, Shahaf"'
The ability to interpret Machine Learning (ML) models is becoming increasingly essential. However, despite significant progress in the field, there remains a lack of rigorous characterization regarding the innate interpretability of different models.
Externí odkaz:
http://arxiv.org/abs/2408.03915
The local and global interpretability of various ML models has been studied extensively in recent years. However, despite significant progress in the field, many known results remain informal or lack sufficient mathematical rigor. We propose a framew
Externí odkaz:
http://arxiv.org/abs/2406.02981
Autor:
Wu, Haoze, Isac, Omri, Zeljić, Aleksandar, Tagomori, Teruhiro, Daggitt, Matthew, Kokke, Wen, Refaeli, Idan, Amir, Guy, Julian, Kyle, Bassan, Shahaf, Huang, Pei, Lahav, Ori, Wu, Min, Zhang, Min, Komendantskaya, Ekaterina, Katz, Guy, Barrett, Clark
This paper serves as a comprehensive system description of version 2.0 of the Marabou framework for formal analysis of neural networks. We discuss the tool's architectural design and highlight the major features and components introduced since its in
Externí odkaz:
http://arxiv.org/abs/2401.14461
Deep neural networks (DNNs) are increasingly being used as controllers in reactive systems. However, DNNs are highly opaque, which renders it difficult to explain and justify their actions. To mitigate this issue, there has been a surge of interest i
Externí odkaz:
http://arxiv.org/abs/2308.00143
Autor:
Bassan, Shahaf, Katz, Guy
With the rapid growth of machine learning, deep neural networks (DNNs) are now being used in numerous domains. Unfortunately, DNNs are "black-boxes", and cannot be interpreted by humans, which is a substantial concern in safety-critical systems. To m
Externí odkaz:
http://arxiv.org/abs/2210.13915
Symbolic music segmentation is the process of dividing symbolic melodies into smaller meaningful groups, such as melodic phrases. We proposed an unsupervised method for segmenting symbolic music. The proposed model is based on an ensemble of temporal
Externí odkaz:
http://arxiv.org/abs/2207.00760