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Örebro University

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Selected work

Representative Papers

Context-free Self-Conditioned GAN for Trajectory Forecasting

Dec 01, 2022International Conference on Machine Learning and Applications

This work addresses the challenge of modeling multimodal behaviors in context-free 2D trajectory prediction by proposing an unsupervised, self-conditioned generative adversarial network (GAN) that requires no external scene information. The method implicitly captures diverse motion patterns through the discriminator’s feature space and incorporates three tailored training strategies to enhance both diversity and accuracy of predictions. As the first study to apply self-conditioned GANs to context-free trajectory forecasting, the model consistently outperforms existing context-free approaches on both human motion and road-agent datasets, demonstrating particularly strong performance on sparsely labeled categories and achieving state-of-the-art results in human motion prediction.

3 citationsRead paper

Embedded Inter-Subject Variability in Adversarial Learning for Inertial Sensor-Based Human Activity Recognition

Aug 31, 2025International Workshop on Machine Learning for Signal Processing

This work addresses the challenge of poor cross-user generalization in inertial sensor-based human activity recognition (HAR) caused by inter-individual variability. To mitigate this issue, the authors propose a novel deep adversarial learning framework that explicitly models and embeds inter-subject differences within the adversarial training process, thereby learning user-invariant feature representations. By enhancing subject invariance, the method effectively reduces the influence of individual-specific characteristics in the learned features. Experimental evaluation on three widely used HAR datasets under leave-one-subject-out (LOSO) cross-validation demonstrates that the proposed approach significantly diminishes inter-subject discrepancies in the feature space and achieves superior recognition accuracy compared to existing state-of-the-art methods in cross-user scenarios.

1 citationsRead paper

Valid Text-to-SQL Generation with Unification-Based DeepStochLog

Mar 17, 2025International Workshop on Neural-Symbolic Learning and Reasoning

To address SQL syntax errors, semantic unexecutability, and logical misalignment between natural language and SQL in text-to-SQL generation, this paper introduces DeepStochLog—a deep probabilistic logic programming framework—to the task for the first time. It intrinsically encodes SQL syntactic constraints via first-order logic unification and integrates neural-symbolic reasoning with grammar-guided decoding, thereby ensuring generated SQL queries are syntactically correct, database-executable, and semantically aligned with the input utterance. Evaluated on the Spider benchmark, our approach improves the valid SQL rate by 12.6%, achieves 100% executability of generated queries, and attains state-of-the-art logical consistency. These results significantly enhance the reliability and practical deployability of text-to-SQL systems.

1 citationsRead paper
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