Olga Saukh

Research output

Research output per year 2017 2019 2020 2021 2022 2023 2024 2024

• 22 Paper
• 11 Preprint
• 8 Conference paper
• 8 Poster
• 8 More
  • 5 Data set/Database
  • 1 Article
  • 1 Habilitation
  • 1 Working paper

Research output per year

Research output per year

Search results

• 2024

  Artifacts (REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints)

  Corti, F., Maag, B., Schauer, J., Pferschy, U. & Saukh, O., 20 Mar 2024

  Research output: Non-print form › Data set/Database

• Artifacts (Subspace-Configurable Networks)

  Wang, D., Saukh, O., He, X. & Thiele, L., 17 Jul 2024

  Research output: Non-print form › Data set/Database

• Breaking the Illusion: Real-world Challenges for Adversarial Patches in Object Detection

  Shack, J., Petrovic, K. & Saukh, O., 10 Dec 2024. 6 p.

  Research output: Contribution to conference › Paper › peer-review

• Breaking the Illusion: Real-world Challenges for Adversarial Patches in Object Detection

  Shack, J., Petrovic, K. & Saukh, O., 23 Oct 2024, arXiv, 21 p.

  Research output: Working paper › Preprint

• DataComp: In search of the next generation of multimodal datasets

  Yitzhak Gadre, S., Ilharco, G., Fang, A., Hayase, J., Smyrnis, G., Nguyen, T., Marten, R., Wortsman, M., Ghosh, D., Zhang, J., Orgad, E., Entezari, R., Daras, G., Pratt, S., Ramanujan, V., Bitton, Y., Marathe, K., Mussmann, S., Vencu, R. & Cherti, M. & 14 others, Krishna, R., Wei Koh, P., Saukh, O., Ratner, A., Song, S., Hajishirzi, H., Farhadi, A., Beaumont, R., Oh, S., G. Dimakis, A., Jitsev, J., Carmon, Y., Shankar, V. & Schmidt, L., 30 May 2024, NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems. p. 27092 - 27112 21 p. 1179. (Advances in Neural Information Processing Systems; vol. 36).

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

  Open Access

• Exploring Human and Artificial Attention Mechanisms in Driving Scenarios

  Rechberger, M., Kraus, D., Priller, P. & Saukh, O., 7 Dec 2024. 6 p.

  Research output: Contribution to conference › Paper › peer-review

  • Artificial Intelligence 100%
  • Language Modeling 100%
  • Attention (Machine Learning) 100%

• HADS: Hardware-Aware Deep Subnetworks

  Corti, F., Maag, B., Schauer, J., Pferschy, U. & Saukh, O., 7 May 2024. 10 p.

  Research output: Contribution to conference › Paper › peer-review

  Open Access

  File

  • Computer Hardware 100%
  • Subnetwork 100%
  • Sparsity 11%
  • Resource Constraint 11%
  • Hardware Platform 11%

• REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints

  Corti, F., Maag, B., Schauer, J., Pferschy, U. & Saukh, O., 20 Mar 2024, p. 1-26, 26 p.

  Research output: Working paper › Preprint

  File

  • Timing Constraint 100%
  • Knapsack 100%

• Sensor-Guided Adaptive Machine Learning on Resource-Constrained Devices

  Papst, F., Kraus, D., Rechberger, M. & Saukh, O., 19 Nov 2024. 8 p.

  Research output: Contribution to conference › Paper › peer-review

  Open Access

• Subspace-Configurable Networks

  Wang, D., Saukh, O., He, X. & Thiele, L., 29 Jul 2024. 31 p.

  Research output: Contribution to conference › Paper › peer-review
• 2023

  Automatic Parameter Exploration for Low-Power Wireless Protocols

  Mohamed Hydher, M. H., Schuß, M., Saukh, O., Boano, C. A. & Römer, K. U., 7 Jul 2023, p. 1-2. 2 p.

  Research output: Contribution to conference › Poster › peer-review

  Open Access

  • Testbed 100%
  • Application Requirement 66%
  • Exhaustive Search 33%
  • Expert Knowledge 33%

• DataComp: In search of the next generation of multimodal datasets

  Yitzhak Gadre, S., Ilharco, G., Fang, A., Hayase, J., Smyrnis, G., Nguyen, T., Marten, R., Wortsman, M., Ghosh, D., Zhang, J., Orgad, E., Entezari, R., Daras, G., Pratt, S., Ramanujan, V., Bitton, Y., Marathe, K., Mussmann, S., Vencu, R. & Cherti, M. & 14 others, Krishna, R., Wei Koh, P., Saukh, O., Ratner, A., Song, S., Hajishirzi, H., Farhadi, A., Beaumont, R., Oh, S., G. Dimakis, A., Jitsev, J., Carmon, Y., Shankar, V. & Schmidt, L., 2023.

  Research output: Working paper › Preprint

• Escaping Adversarial Attacks with Egyptian Mirrors

  Saukh, O., 6 Oct 2023. 6 p.

  Research output: Contribution to conference › Paper › peer-review

• Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images

  Cao, N. & Saukh, O., 18 Nov 2023, arXiv, 16 p.

  Research output: Working paper › Preprint

• Mitigating Distribution Shifts in Pollen Classification from Microscopic Images Using Geometric Data Augmentations

  Cao, N. & Saukh, O., 16 Dec 2023. 9 p.

  Research output: Contribution to conference › Paper › peer-review

• Poster: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints on IoT Devices

  Corti, F., Maag, B., Hinterer, C., Rudolf, J., Schauer, J. & Saukh, O., 7 Jul 2023, p. 1-2. 2 p.

  Research output: Contribution to conference › Poster › peer-review

  Open Access

  File

• REPAIR: REnormalizing Permuted Activations for Interpolation Repair

  Jordan, K., Sedghi, H., Saukh, O., Entezari, R. & Neyshabur, B., 20 Jan 2023, The Eleventh International Conference on Learning Representations.

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

  Open Access

  • Variance 100%
  • Neural Network 100%
  • Normalization Layer 100%
  • Solution and Solubility 100%
  • Permutation 33%

• Subspace-Configurable Networks

  Saukh, O., Wang, D., He, X. & Thiele, L., 21 Nov 2023, arXiv, 36 p.

  Research output: Working paper › Preprint

  File

  • Deep Learning Method 100%
  • Adaptation Models 100%
  • Reliability Availability and Maintainability (Reliability Engineering) 100%
  • Internet-Of-Things 100%
  • Offline Training 100%

• The Role of Pre-training Data in Transfer Learning

  Entezari, R., Wortsman, M., Saukh, O., Shariatnia, M. M., Sedghi, H. & Schmidt, L., 27 Feb 2023. 38 p.

  Research output: Contribution to conference › Paper

  • Learning Transfer 100%
  • Learning Paradigm 100%
• 2022

  Challenges of Integration and Validation of Farm and Sensor Data for Dairy Herd Management

  Schodl, K., Fuerst-Waltl, B., Schwarzenbacher, H., Steininger, F., Suntinger, M., Papst, F., Saukh, O., Lemmens, L. & Egger-Danner, C., 2022, Share. Connect. Transform. Sharing Solutions on Digital Transformation, Animal Welfare and Environmental Sustainability to Support Global Food Security. Proceedings of the 45th ICAR Annual Conference.

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

• Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images

  Mahbod, A., Entezari, R., Ellinger, I. & Saukh, O., 16 Sept 2022, Applications of Medical Artificial Intelligence. AMAI 2022. p. 108 - 117 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13540).

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

• Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images

  Mahbod, A., Entezari, R., Ellinger, I. & Saukh, O., 15 Jun 2022.

  Research output: Working paper › Preprint

  • Histological Image 100%
  • Deep Neural Network 100%
  • Main Branch 50%

• How well do contrastively trained models transfer?

  Shariatnia, M. M., Entezari, R., Wortsman, M., Saukh, O. & Schmidt, L., 23 Jul 2022. 8 p.

  Research output: Contribution to conference › Paper › peer-review

  Open Access

  • Large Data Set 100%
  • Training Objective 100%
  • Training Methods 100%

• REPAIR: REnormalizing Permuted Activations for Interpolation Repair

  Jordan, K., Sedghi, H., Saukh, O., Entezari, R. & Neyshabur, B., 14 Dec 2022.

  Research output: Working paper › Preprint

  • Neural Network 100%
  • Network Layer 100%
  • Linear Interpolation 100%
  • ResNet50 100%
  • Normalization Layer 100%

• SensorFormer: Efficient Many-to-Many Sensor Calibration with Learnable Input Sub-Sampling

  Cheng, Y., Saukh, O. & Thiele, L., 15 Oct 2022, In: IEEE Internet of Things Journal. 9, 20, p. 20577-20589 13 p.

  Research output: Contribution to journal › Article › peer-review

  • State of the Art 100%
  • Sensor Calibration 100%
  • Past Measurement 100%
  • Simple Linear Regression 100%
  • Accuracy Improvement 100%

• Studying the impact of magnitude pruning on contrastive learning methods

  Corti, F., Entezari, R., Bacciu, D., Hooker, S. & Saukh, O., 14 Jun 2022. 9 p.

  Research output: Contribution to conference › Poster › peer-review

  • Contrastive Learning 100%
  • Neural Network 100%
  • Traditional Cross 50%
  • Contrastive Loss 50%

• Studying the impact of magnitude pruning on contrastive learning methods

  Corti, F., Entezari, R., Bacciu, D., Hooker, S. & Saukh, O., 1 Jul 2022, arXiv, p. 1-10, 10 p.

  Research output: Working paper › Preprint

  • Sparsity 100%
  • Contrastive Learning 100%
  • Neural Network 100%
  • Training Phase 50%
  • Learning Result 50%

• To Share or Not to Share: On Location Privacy in IoT Sensor Data

  Papst, F., Stricker, N., Entezari, R. & Saukh, O., 4 May 2022, Proceedings - 7th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2022. IEEE, p. 128-140 13 p. (Proceedings - 7th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2022).

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
• 2021

  Compensating Altered Sensitivity of Duty-Cycled MOX Gas Sensors with Machine Learning

  Gherman, M.-P., Cheng, Y., Gomez, A. & Saukh, O., 2021, 2021 18th IEEE International Conference on Sensing, Communication and Networking, SECON 2021. 9 p. 9491586

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

  • Air Quality 100%
  • Gas Sensor 100%
  • Learning System 100%
  • Electric Resistance 100%
  • Cross Allergy 100%

• Exploring Co-dependency of IoT Data Quality and Model Robustness in Precision Cattle Farming

  Papst, F., Schodl, K. & Saukh, O., 15 Nov 2021, p. 433-438. 6 p.

  Research output: Contribution to conference › Paper › peer-review

  Open Access

  • Internet-Of-Things 100%
  • Predictive Model 100%
  • Data Quality 100%
  • Agriculture 100%
  • Use Case 50%

• Interpretable and Transferable Models to Understand the Impact of Lockdown Measures on Local Air Quality

  Einsiedler, J., Cheng, Y., Papst, F. & Saukh, O., 14 Jul 2021. 8 p.

  Research output: Contribution to conference › Paper › peer-review

  Open Access

• The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks

  Entezari, R., Sedghi, H., Saukh, O. & Neyshabur, B., 12 Oct 2021, arXiv, 24 p.

  Research output: Working paper › Preprint

  • Neural Network 100%
  • Permutation 100%
  • Linear Interpolation 50%
  • Shortfall 50%
  • Ensemble Method 50%

• The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks

  Entezari, R., Sedghi, H., Saukh, O. & Neyshabur, B., 7 Jul 2021.

  Research output: Contribution to conference › Poster › peer-review

  • Neural Network 100%
  • Permutation 100%
  • Linear Interpolation 50%
  • Shortfall 50%

• TIP-Air: Tracking Pollution Transfer for Accurate Air Quality Prediction

  Cheng, Y., Saukh, O. & Thiele, L., 21 Sept 2021, p. 589-599. 11 p.

  Research output: Contribution to conference › Paper › peer-review

  Open Access

  • Seq2Seq 100%
  • Learning Trajectory 100%
  • Air Mass 20%
  • Pollution Policy 20%

• Towards On-demand Gas Sensing

  Gherman, M.-P., Cheng, Y., Gomez, A. & Saukh, O., 14 Jul 2021. 3 p.

  Research output: Contribution to conference › Poster › peer-review

• Understanding the effect of sparsity on neural networks robustness

  Timpl, L., Entezari, R., Sedghi, H., Neyshabur, B. & Saukh, O., 24 Jul 2021.

  Research output: Working paper › Preprint

  Open Access

• Understanding the effect of sparsity on neural networks' robustness

  Timpl, L., Entezari, R., Sedghi, H., Neyshabur, B. & Saukh, O., 8 Jul 2021.

  Research output: Contribution to conference › Poster › peer-review

  Open Access
• 2020

  Automated Pollen Detection with an Affordable Technology

  Nam, C. N. K., Meyer, M., Thiele, L. & Saukh, O., 19 Feb 2020. 12 p.

  Research output: Contribution to conference › Paper › peer-review

  • Flow Velocity 100%
  • Global Warming 100%
  • Weather Condition 100%
  • Air Flow 100%
  • Individual Layer 100%

• Class-dependent Compression of Deep Neural Networks

  Entezari, R. & Saukh, O., 21 Apr 2020. 6 p.

  Research output: Contribution to conference › Paper › peer-review

  Open Access

• Class-dependent Compression of Deep Neural Networks

  Entezari, R. & Saukh, O., 19 Apr 2020.

  Research output: Working paper › Preprint

• Dataset: Pollen Video Library for Benchmarking Detection, Classification, Tracking and Novelty Detection Tasks

  Nam, C. N. K., Meyer, M., Thiele, L. & Saukh, O., 16 Nov 2020. 3 p.

  Research output: Contribution to conference › Paper › peer-review

  • Local Distribution 100%
  • Recorded Image 100%
  • Collectors 100%
  • Physical Environment 100%

• Embedded Sensing Systems: Design, Models, Algorithms, and Applications

  Saukh, O., 5 May 2020, 254 p.

  Research output: Thesis › Habilitation

• Localization from Activity Sensor Data: poster abstract

  Papst, F., Stricker, N. & Saukh, O., 16 Nov 2020, p. 703-704. 2 p.

  Research output: Contribution to conference › Poster › peer-review

  Open Access

• Microscopic Images of Pollen captured in Graz, Austria

  Nam, C. N. K. (Photographer), Meyer, M. (Photographer) & Saukh, O. (Photographer), 19 Feb 2020

  Research output: Non-print form › Data set/Database

  Open Access

• PhD Forum Abstract: Understanding Deep Model Compression for IoT Devices

  Entezari, R. & Saukh, O., 21 Apr 2020, Proceedings - 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020. p. 385-386 2 p. 9110987. (Proceedings - 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020).

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

  • Deep Understanding 100%
  • Internet of Things Device 100%
  • Deep Neural Network 100%
  • Model Compression 100%
  • Neural Network 100%

• Technical Report: Interpretable and Transferable Models to Understand the Impact of Lockdown Measures on Local Air Quality

  Einsiedler, J., Cheng, Y., Papst, F. & Saukh, O., 19 Nov 2020, 12 p.

  Research output: Working paper
• 2019

  Deep and Efficient Impact Models for Edge Characterization and Control of Energy Events

  Stamatescu, G., Entezari, R., Römer, K. U. & Saukh, O., 2019, 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

  File

  • Failure Event 100%
  • Demand Side Response 66%
  • Network Control 50%
  • Historical Data 50%
  • Public Data Set 50%

• Embracing Opportunities of Livestock Big Data Integration with Privacy Constraints

  Papst, F., Saukh, O., Römer, K. U., Grandl, F., Jakovljevic, I., Steiniger, F., Mayerhofer, M., Duda, J. & Egger-Danner, C., 22 Oct 2019. 4 p.

  Research output: Contribution to conference › Paper › peer-review

  • Product Quality 100%
  • Prevent Disease 50%
  • Integration Architecture 50%
  • Health Issue 50%

• Internet of Cows - Opportunities and Challenges for Improving Health, Welfare and Efficiency in Dairying

  Egger-Danner, C., Fürst-Waltl, B., Klimek, P., Saukh, O. & Wittek, T., 17 Jun 2019.

  Research output: Contribution to conference › Paper › peer-review

  • Cow 100%
  • Animal Health 100%
  • Veterinary Diagnostic Medicine 100%
  • Claws 100%
  • Food Safety 100%

• Opportunities and Challenges of New Technologies for Performance Recording with Focus on Claw Health and Metabolism

  Egger-Danner, C., Suntiger, M., Grandl, F., Majcen, P., Mayerhofer, M., Papst, F., Saukh, O., Fallast, M., Turkaspa, A., Steiniger, F., Linke, K., Duda, J., Wittek, T., Fuerst-Waltl, B., Auer, F. J. & Kofler, J., 17 Jun 2019.

  Research output: Contribution to conference › Paper › peer-review

  • Dairy Industry 100%
  • Ketoacidosis 33%