E-LIB Suche - Ergebnisse für: Zalmanovici, Marcel

1

FreaAI: Automated Extraction of Data Slices to Test Machine..:

, In: Communications in Computer and Information Science; Engineering Dependable and Secure Machine Learning Systems,

Ackerman, Samuel ; Raz, Orna ; Zalmanovici, Marcel - p. 67-83 , 2020

2

Bridging the gap between ML solutions and their business re..:

, In: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering,

Barash, Guy ; Farchi, Eitan ; Jayaraman, Ilan... - p. 1048-1058 , 2019

Link: https://dl.acm.org/doi/1..

3

Governance and Regulations Implications on Machine Learning..:

, In: Lecture Notes in Computer Science; Cyber Security Cryptography and Machine Learning,

Nadler, Sima ; Raz, Orna ; Zalmanovici, Marcel - p. 208-211 , 2019

Link: https://doi.org/10.1007/..

4

Proactive and pervasive combinatorial testing:

, In: Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Practice,

Blue, Dale ; Raz, Orna ; Tzoref-Brill, Rachel.. - p. 144-152 , 2018

Link: https://dl.acm.org/doi/1..

5

Cluster-based test suite functional analysis:

, In: Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering,

Zalmanovici, Marcel ; Raz, Orna ; Tzoref-Brill, Rachel - p. 962-967 , 2016

Link: https://dl.acm.org/doi/1..

6

Coverage-based metrics for cloud adaptation:

, In: Proceedings of the 2nd International Workshop on Quality-Aware DevOps,

Magid, Yonit ; Tzoref-Brill, Rachel ; Zalmanovici, Marcel - p. 1-6 , 2016

Link: https://dl.acm.org/doi/1..

7

JIT technology with C/C++: Feedback-directed dynamic recomp..:

Nuzman, Dorit ; Eres, Revital ; Dyshel, Sergei..
ACM Transactions on Architecture and Code Optimization. 10 (2013) 4 - p. 1-25 , 2013

Link: https://doi.org/10.1145/..

8

JIT technology with C/C++ : Feedback-directed dynamic re..:

Nuzman, Dorit ; Eres, Revital ; Dyshel, Sergei..
ACM Transactions on Architecture and Code Optimization (TACO). 10 (2013) 4 - p. 1-25 , 2013

Link: https://dl.acm.org/doi/1..

9

Refactoring techniques for aggressive object inlining in Ja..:

Ben Asher, Yosi ; Gal, Tomer ; Haber, Gadi.
Automated Software Engineering. 19 (2011) 1 - p. 97-136 , 2011

Link: https://doi.org/10.1007/..

10

Performance analysis and visualization tools for cell/B.E. ..:

, In: Proceedings of the 1st international forum on Next-generation multicore/manycore technologies,

Vianney, Duc ; Haber, Gad ; Heilper, Andre. - p. 1-12 , 2008

Link: https://dl.acm.org/doi/1..

11

Unveiling Safety Vulnerabilities of Large Language Models:

Kour, George ; Zalmanovici, Marcel ; Zwerdling, Naama...
http://arxiv.org/abs/2311.04124. , 2023

Link: http://arxiv.org/abs/231..

12

High-quality Conversational Systems:

Ackerman, Samuel ; Anaby-Tavor, Ateret ; Farchi, Eitan...
http://arxiv.org/abs/2204.13043. , 2022

Link: http://arxiv.org/abs/220..

13

Classifier Data Quality: A Geometric Complexity Based Metho..:

Kour, George ; Zalmanovici, Marcel ; Raz, Orna..
http://arxiv.org/abs/2112.11832. , 2021

Link: http://arxiv.org/abs/211..

14

Automatically detecting data drift in machine learning clas..:

Ackerman, Samuel ; Raz, Orna ; Zalmanovici, Marcel.
http://arxiv.org/abs/2111.05672. , 2021

Link: http://arxiv.org/abs/211..

15

Machine Learning Model Drift Detection Via Weak Data Slices:

Ackerman, Samuel ; Dube, Parijat ; Farchi, Eitan..
http://arxiv.org/abs/2108.05319. , 2021

Link: http://arxiv.org/abs/210..