@inproceedings{52744, author = {{Jafarzadeh, Hanieh and Klemme, Florian and Amrouch, Hussam and Hellebrand, Sybille and Wunderlich, Hans-Joachim}}, booktitle = {{European Test Symposium, The Hague, Netherlands, May 20-24, 2024}}, location = {{The Hague, NL}}, pages = {{6}}, publisher = {{IEEE}}, title = {{{Time and Space Optimized Storage-based BIST under Multiple Voltages and Variations}}}, year = {{2024}}, } @inproceedings{52742, author = {{Jafarzadeh, Hanieh and Klemme, Florian and Amrouch, Hussam and Hellebrand, Sybille and Wunderlich, Hans-Joachim}}, booktitle = {{IEEE Latin American Test Symposium (LATS), Maceió, Brazil, April 9-12, 2024}}, location = {{Maceió}}, pages = {{6}}, publisher = {{IEEE}}, title = {{{Vmin Testing under Variations: Defect vs. Fault Coverage}}}, year = {{2024}}, } @inproceedings{52743, author = {{Hellebrand, Sybille and Sadeghi-Kohan, Somayeh and Wunderlich, Hans-Joachim}}, booktitle = {{International Symposium of EDA (ISEDA), Xi'an, China, May 10-13, 2024}}, location = {{Xi'an, China}}, pages = {{1}}, title = {{{Functional Safety and Reliability of Interconnects throughout the Silicon Life Cycle}}}, year = {{2024}}, } @inproceedings{48387, author = {{Lebedeva, Anastasia and Protte, Marius and van Straaten, Dirk and Fahr, René}}, booktitle = {{Advances in Information and Communication}}, location = {{Berlin}}, pages = {{178–204}}, publisher = {{Springer, Cham}}, title = {{{Involvement of domain experts in the AI training does not affect adherence – An AutoML study}}}, doi = {{https://doi.org/10.1007/978-3-031-53960-2_13}}, volume = {{919}}, year = {{2024}}, } @inproceedings{52745, author = {{Wunderlich, Hans-Joachim and Jafarzadeh, Hanieh and Hellebrand, Sybille}}, booktitle = {{International Symposium of EDA (ISEDA), Xi’an, China, May 10-13, 2024}}, location = {{Xi’an, China}}, pages = {{1}}, title = {{{Robust Test of Small Delay Faults under PVT-Variations}}}, year = {{2024}}, } @misc{50284, author = {{Stiballe, Alisa and Reimer, Jan Dennis and Sadeghi-Kohan, Somayeh and Hellebrand, Sybille}}, publisher = {{37. ITG / GMM / GI -Workshop "Testmethoden und Zuverlässigkeit von Schaltungen und Systemen" (TuZ'24), Feb. 2024}}, title = {{{Modeling Crosstalk-induced Interconnect Delay with Polynomial Regression}}}, year = {{2024}}, } @misc{51799, author = {{Ustimova, Magdalina and Sadeghi-Kohan, Somayeh and Hellebrand, Sybille}}, publisher = {{37. ITG / GMM / GI -Workshop "Testmethoden und Zuverlässigkeit von Schaltungen und Systemen" (TuZ'24), Feb. 2024}}, title = {{{Crosstalk-Aware Simulation of Interconnects Using Artificial Neural Networks}}}, year = {{2024}}, } @article{52758, author = {{Harder, Hans and Peitz, Sebastian}}, title = {{{On the continuity and smoothness of the value function in reinforcement learning and optimal control}}}, year = {{2024}}, } @inproceedings{52827, author = {{Hu, Lijie and Habernal, Ivan and Shen, Lei and Wang, Di}}, booktitle = {{Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024}}, editor = {{Graham, Yvette and Purver, Matthew}}, pages = {{478–499}}, publisher = {{Association for Computational Linguistics}}, title = {{{Differentially Private Natural Language Models: Recent Advances and Future Directions}}}, year = {{2024}}, } @inproceedings{52842, abstract = {{Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems. Differentially private stochastic gradient descent (DP-SGD) is a popular method for training machine learning models with concrete privacy guarantees; however, the implementation specifics of training a model with DP-SGD are not always clarified in existing models, with differing software libraries used and code bases not always being public, leading to reproducibility issues. To tackle this, we introduce DP-NMT, an open-source framework for carrying out research on privacy-preserving NMT with DP-SGD, bringing together numerous models, datasets, and evaluation metrics in one systematic software package. Our goal is to provide a platform for researchers to advance the development of privacy-preserving NMT systems, keeping the specific details of the DP-SGD algorithm transparent and intuitive to implement. We run a set of experiments on datasets from both general and privacy-related domains to demonstrate our framework in use. We make our framework publicly available and welcome feedback from the community.}}, author = {{Igamberdiev, Timour and Vu, Doan Nam Long and Kuennecke, Felix and Yu, Zhuo and Holmer, Jannik and Habernal, Ivan}}, booktitle = {{Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations}}, editor = {{Aletras, Nikolaos and De Clercq, Orphee}}, pages = {{94–105}}, publisher = {{Association for Computational Linguistics}}, title = {{{DP-NMT: Scalable Differentially Private Machine Translation}}}, year = {{2024}}, }