Jesper Tegnér — accepted AI-conference papers

2025

Interpretable Causal Representation Learning for Biological Data in the Pathway Space

Jesus de la Fuente Cedeño, Robert Lehmann, Carlos Ruiz-Arenas, Jan Voges, Irene Marín-Goñi, Xabier Martinez de Morentin +5

ICLR 2025poster

Predicting the impact of genomic and drug perturbations in cellular function is crucial for understanding gene functions and drug effects, ultimately leading to improved therapies. To this end, Causal Representation Learning (CRL) constitutes one of the most promising approaches, as it aims to ident…

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2023

Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition

Srijith Radhakrishnan, Chao-Han Huck Yang, Sumeer Ahmad Khan, Rohit Kumar, Narsis A. Kiani, David Gomez-Cabrero +1

EMNLP 2023short main

We introduce a new cross-modal fusion technique designed for generative error correction in automatic speech recognition (ASR). Our methodology leverages both acoustic information and external linguistic representations to generate accurate speech transcription contexts. This marks a step towards a…

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2020

Interpretable Self-Attention Temporal Reasoning for Driving Behavior Understanding

Yi-Chieh Liu, Yung-An Hsieh, Min-Hung Chen, Chao-Han Huck Yang, Jesper Tegnér, Yi-Chang James Tsai

ICASSP 2020accepted

Performing driving behaviors based on causal reasoning is essential to ensure driving safety. In this work, we investigated how state-of-the-art 3D Convolutional Neural Networks (CNNs) perform on classifying driving behaviors based on causal reasoning. We proposed a perturbation-based visual explana…

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