Erik Schultheis
Orcid: 0000-0003-1685-8397
According to our database1,
Erik Schultheis
authored at least 16 papers
between 2020 and 2024.
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Bibliography
2024
Learning label-label correlations in Extreme Multi-label Classification via Label Features.
CoRR, 2024
CoRR, 2024
2023
Generating artificial displacement data of cracked specimen using physics-guided adversarial networks.
Mach. Learn. Sci. Technol., December, 2023
Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 500k Labels on a Single Commodity GPU.
CoRR, 2023
Physics-guided adversarial networks for artificial digital image correlation data generation.
CoRR, 2023
Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 670k Labels on a Single Commodity GPU.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023
Generalized test utilities for long-tail performance in extreme multi-label classification.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
2022
Speeding-up one-versus-all training for extreme classification via mean-separating initialization.
Mach. Learn., 2022
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022
Proceedings of the Machine Learning under Resource Constraints - Volume 1: Fundamentals, 2022
2021
CoRR, 2021
CoRR, 2021
Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels.
Proceedings of the WWW '21: The Web Conference 2021, 2021
2020