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| Sparse Autoencoders for Speech Modeling and Recognition, , École polytechnique fédérale de Lausanne, 2023 |
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| SPARSE AUTOENCODERS TO ENHANCE SPEECH RECOGNITION, and , Idiap-RR-10-2022 |
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| From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition, and , in: Proceedings of Interspeech Conference, 2022 |
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| SPEECH MODELING USING SPARSE AUTOENCODERS, and , Idiap-RR-11-2022 |
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| On Learning to Identify Genders from Raw Speech Signal Using CNNs, , and , in: Proceedings of Interspeech, Hyderabad, INDIA, pages 287-291, 2018 |
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| Modeling dominance effects on nonverbal behaviors using granger causality, , , , , and , in: Proceedings of International Conference on Multimodal Interaction, ICMI 2012, Santa Monica, CA, 2012 |
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| Towards interpretable emotion recognition: Identifying key features with machine learning, and , in: Forum Acusticum/EuroNoise, Malaga, Spain, 2025 |
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| Multiview Canonical Correlation Analysis for Automatic Pathological Speech Detection, , and , in: International Conference on Acoustics, Speech and Signal Processing, Hyderabad, India, IEEE, 2025 |
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| Unsupervised Learning for Information Distillation, , Idiap-RR-47-2007 |
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| Understanding the Social Context of Eating with Multimodal Smartphone Sensing: The Role of Country Diversity, , and , in: 25th ACM International Conference on Multimodal Interaction, 2023 |
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| Evaluation of SVM Binary Classification with Nonparametric Stochastic Simulations, , Idiap-RR-07-2001 |
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| Spatial Data Mapping with Support Vector Regression, and , Idiap-RR-09-2000 |
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| Numerical Experiments with Support Vector Machines, and , Idiap-RR-15-1999 |
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| Environmental spatial data classification with Support Vector Machines, , , and , Idiap-RR-07-1999 |
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| Advanced Spatial Data Analysis and Modelling with Support Vector Machines, , , and , Idiap-RR-31-2000 |
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| Support Vector Machines for Classification and Mapping of Reservoir Data, , , , , and , Idiap-RR-04-2001 |
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| Environmental Data Mapping with Support Vector Regression and Geostatistics, , and , Idiap-RR-10-2000 |
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| Reading Companion: The Technical and Social Design of an Automated Reading Tutor, , , , , and , in: Workshop on Child, Computer and Interaction, Portland, Oregon, U.S.A., 2012 |
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| Modeling Annotator Behaviors for Crowd Labeling, , , and , in: Neurocomputing, 160:141–156, 2015 |
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| Real-Time DCT Learning-based Reconstruction of Neural Signals, , and , in: EUSIPCO, 2018 |
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| Variational Information Bottleneck for Effective Low-Resource Fine-Tuning, , and , in: ICLR, 2021 |
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| End-to-End Bias Mitigation by Modelling Biases in Corpora, , and , in: ACL, 2020 |
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| Compacter: Efficient Low-Rank Hypercomplex Adapter Layers, , and , in: NeurIPS, 2021 |
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| TESS: Text-to-text selfconditioned simplex diffusion, , , , , , and , in: Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2347–2361, Association for Computational Linguistics, 2024 |
| A Learning-Based Framework for Quantized Compressed Sensing, , and , in: A Learning-Based Framework for Quantized Compressed Sensing, 2019 |
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| Learning Entailment-Based Sentence Embeddings from Natural Language Inference, , and , Idiap-RR-20-2019 |
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| Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks, , , and , in: ACL, 2021 |
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| PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models, , , , , , and , in: ACL, 2022 |
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| Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models, , École Polytechnique Fédérale de Lausanne, 2022 |
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| Processing Megapixel Images with Deep Attention-Sampling Models, and , Idiap-RR-07-2019 |
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| Processing Megapixel Images with Deep Attention-Sampling Models, and , in: Proceedings of International Conference on Machine Learning, 2019 |
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| Not All Samples Are Created Equal: Deep Learning with Importance Sampling, and , Idiap-RR-12-2018 |
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| Not All Samples Are Created Equal: Deep Learning with Importance Sampling, and , in: Proceedings of International Conference on Machine Learning, 2018 |
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| Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention, , , and , in: Proceedings of International Conference on Machine Learning, 2020 |
| Haptic Feedback Compared with Visual Feedback for BCI, , , , , and , in: Proceedings of the 3rd International Brain-Computer Interface Workshop & Training Course 2006, 2006 |
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| Combining Wavelet-domain Hidden Markov Trees with Hidden Markov Models, , and , Idiap-RR-14-1999 |
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| Machine Learning Approaches to Text Representation using Unlabeled Data, , Ecole Polytechnique Fédérale de Lausanne, 2006 |
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| Machine Learning Approaches to Text Representation using Unlabeled Data, , Idiap-RR-76-2006 |
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| A Multitask Learning Approach to Document Representation using Unlabeled Data, and , Idiap-RR-44-2006 |
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| A Neural Network for Text Representation, and , in: International Conference on Artificial Neural Networks, ICANN, 2005 |
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| A Neural Network for Text Representation, and , Idiap-RR-12-2005 |
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| Theme Topic Mixture Model: A Graphical Model for Document Representation, and , in: Pascal Workshop on Text Mining and Understanding, 2004 |
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| Theme Topic Mixture Model: A Graphical Model for Document Representation, and , Idiap-RR-05-2004 |
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| Textual Data Representation, and , Idiap-RR-74-2003 |
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| Benchmarking Non-Parametric Statistical Tests, , and , in: Advances in Neural Information Processing Systems, NIPS 18. MIT Press, 2005 |
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| Benchmarking Non-Parametric Statistical Tests, , and , Idiap-RR-38-2005 |
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| {S}ignificance {T}ests for {\em Bizarre} {M}easures in 2-{C}lass {C}lassification {T}asks, , and , Idiap-RR-34-2004 |
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| Active Shape Models Using Local Binary Patterns, and , Idiap-RR-07-2006 |
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| A comparison of noise reduction techniques for robust speech recognition, , Idiap-RR-10-1999 |
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| Towards introducing long-term statistics in MUSE for robust speech recognition, and , in: Automatic Speech Recognition and Understanding (ASRU) workshop, 1999 |
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