Olga Saukh

Publikationen

Publikationen pro Jahr 2017 2019 2020 2021 2022 2023 2024 2024

• 22 Paper
• 11 Preprint
• 8 Beitrag in einem Konferenzband
• 8 Poster
• 8 Mehr
  • 5 Datensätze / Datenbank
  • 1 Artikel
  • 1 Habilitation
  • 1 Working paper

Publikationen pro Jahr

Publikationen pro Jahr

Suchergebnisse

• 2024

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

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

  Publikation: Nonprint-Formen › Datensätze / Datenbank

• Artifacts (Subspace-Configurable Networks)

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

  Publikation: Nonprint-Formen › Datensätze / Datenbank

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

  Shack, J., Petrovic, K. & Saukh, O., 23 Okt. 2024, arXiv, 21 S.

  Publikation: Arbeitspapier › Preprint

  • Object Detection 100%
  • Adversarial Machine Learning 100%
  • Learning System 100%
  • Machine Learning 50%
  • Physical World 50%

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

  Shack, J., Petrovic, K. & Saukh, O., 10 Dez. 2024. 6 S.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  • Learning System 100%
  • Transformation Parameter 50%
  • Computervision 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 mehr, 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 Mai 2024, NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems. S. 27092 - 27112 21 S. 1179. (Advances in Neural Information Processing Systems; Band 36).

  Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

  Open Access

  • Machine Learning 100%
  • Learning System 100%
  • Data Source 100%
  • Critical Component 100%
  • Learning Ecosystem 100%

• Exploring Human and Artificial Attention Mechanisms in Driving Scenarios

  Rechberger, M., Kraus, D., Priller, P. & Saukh, O., 7 Dez. 2024. 6 S.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  • 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 Mai 2024. 10 S.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  Open Access

  Datei

  • 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 März 2024, S. 1-26, 26 S.

  Publikation: Arbeitspapier › Preprint

  Datei

  • 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 S.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  Open Access

  • Machine Learning 100%
  • Learning System 100%
  • Constrained Device 100%
  • Deep Learning Model 100%
  • Deep Learning Method 100%

• Subspace-Configurable Networks

  Wang, D., Saukh, O., He, X. & Thiele, L., 29 Juli 2024. 31 S.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  • Model Adaptation 100%
  • Computing Resource 100%
  • Storage Resource 100%
  • Data Augmentation 100%
  • Sensor Placement 100%
• 2023

  Automatic Parameter Exploration for Low-Power Wireless Protocols

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

  Publikation: Konferenzbeitrag › Poster › Begutachtung

  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 mehr, 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.

  Publikation: Arbeitspapier › Preprint

  • Generative Pre-Trained Transformer 4 100%

• Escaping Adversarial Attacks with Egyptian Mirrors

  Saukh, O., 6 Okt. 2023. 6 S.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  • Adversarial Machine Learning 100%
  • Adversarial Example 100%

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

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

  Publikation: Arbeitspapier › Preprint

  • Test Data 100%
  • Geometric Feature 100%
  • Test Dataset 100%
  • Learning System 100%
  • Field Data 100%

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

  Cao, N. & Saukh, O., 16 Dez. 2023. 9 S.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  • Data Augmentation 100%
  • Microscopic Image 100%
  • Texture Information 100%
  • Learning System 100%
  • Machine Learning 50%

• 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 Juli 2023, S. 1-2. 2 S.

  Publikation: Konferenzbeitrag › Poster › Begutachtung

  Open Access

  Datei

  • Internet-Of-Things 100%
  • Resource Constraint 100%
  • Subnetwork 100%
  • Machine Learning 33%
  • Learning System 33%

• 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.

  Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

  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 S.

  Publikation: Arbeitspapier › Preprint

  Datei

  • 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 S.

  Publikation: Konferenzbeitrag › 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.

  Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

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

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

  Publikation: Arbeitspapier › Preprint

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

• 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. S. 108 - 117 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13540).

  Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

  • Instance Segmentation 100%
  • Deep Neural Network 100%
  • Histological Image 100%
  • Compression Ratio 100%
  • Medical Image 50%

• How well do contrastively trained models transfer?

  Shariatnia, M. M., Entezari, R., Wortsman, M., Saukh, O. & Schmidt, L., 23 Juli 2022. 8 S.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  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 Dez. 2022.

  Publikation: Arbeitspapier › 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 Okt. 2022, in: IEEE Internet of Things Journal. 9, 20, S. 20577-20589 13 S.

  Publikation: Beitrag in einer Fachzeitschrift › Artikel › Begutachtung

  • 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., 1 Juli 2022, arXiv, S. 1-10, 10 S.

  Publikation: Arbeitspapier › Preprint

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

• Studying the impact of magnitude pruning on contrastive learning methods

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

  Publikation: Konferenzbeitrag › Poster › Begutachtung

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

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

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

  Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

  • Location Privacy 100%
  • Pseudonymization 50%
• 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 S. 9491586

  Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

  • 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, S. 433-438. 6 S.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  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 Juli 2021. 8 S.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  Open Access

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

  Entezari, R., Sedghi, H., Saukh, O. & Neyshabur, B., 12 Okt. 2021, arXiv, 24 S.

  Publikation: Arbeitspapier › 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 Juli 2021.

  Publikation: Konferenzbeitrag › Poster › Begutachtung

  • 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, S. 589-599. 11 S.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  Open Access

  • Seq2Seq 100%
  • Learning Trajectory 100%
  • Local Decision 20%
  • Air Mass 20%
  • Pollution Control 20%

• Towards On-demand Gas Sensing

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

  Publikation: Konferenzbeitrag › Poster › Begutachtung

• Understanding the effect of sparsity on neural networks' robustness

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

  Publikation: Konferenzbeitrag › Poster › Begutachtung

  Open Access

  • Network Capacity 100%
  • Adversarial Example 100%
  • Data Corruption 100%
  • Compression Network 100%

• Understanding the effect of sparsity on neural networks robustness

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

  Publikation: Arbeitspapier › Preprint

  Open Access
• 2020

  Automated Pollen Detection with an Affordable Technology

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

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  • 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., 19 Apr. 2020.

  Publikation: Arbeitspapier › Preprint

  • Deep Neural Network 100%
  • False Negative 100%
  • False Positive 66%
  • Labeled Instance 66%
  • World Application 33%

• Class-dependent Compression of Deep Neural Networks

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

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  Open Access

  • Labeled Instance 100%
  • Class Imbalance Problem 49%
  • Model Compression 49%
  • Subnetwork 49%

• 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 S.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

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

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

  Saukh, O., 5 Mai 2020, 254 S.

  Publikation: Studienabschlussarbeit › Habilitation

• Localization from Activity Sensor Data: poster abstract

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

  Publikation: Konferenzbeitrag › Poster › Begutachtung

  Open Access

  • Environmental Data 100%
  • Average Accuracy 100%

• Microscopic Images of Pollen captured in Graz, Austria

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

  Publikation: Nonprint-Formen › Datensätze / Datenbank

  Open Access

  • Grazer 100%

• 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. S. 385-386 2 S. 9110987. (Proceedings - 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020).

  Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

  • 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 S.

  Publikation: Arbeitspapier › 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 S.

  Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

  Datei

  • 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 Okt. 2019. 4 S.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  • 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 Juni 2019.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  • 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 Juni 2019.

  Publikation: Konferenzbeitrag › Paper › Begutachtung

  • Dairy Industry 100%
  • Ketoacidosis 33%