Published in COLM, 2024
We propose a method to learn planning for language modeling using unlabeled data.
Recommended citation: Nathan Cornille, Marie-Francine Moens, Florian Mai. (2024). "Learning to Plan for Language Modeling from Unlabeled Data." COLM 2024. https://arxiv.org/abs/2404.00614
Published in ACL, 2023
We propose an efficient all-MLP architecture with the same inductive biases as Transformers.
Recommended citation: Florian Mai, Arnaud Pannatier, Fabio Fehr, Haolin Chen, François Marelli, François Fleuret and James Henderson. (2020). "HyperMixer: An MLP-based Low Cost Alternative to Transformers." ACL 2023. https://arxiv.org/abs/2203.03691
Published in AACL, 2022
We embed text into a variable-size embedding space via an autoencoder and propose a method to learn unsupervised conditional text generation tasks in that space.
Recommended citation: Florian Mai and James Henderson. (2022). "Bag-of-Vectors Autoencoders for Unsupervised Conditional Text Generation." AACL 2022. https://arxiv.org/abs/2110.07002
Published in EMNLP, 2020
We reduce conditional text generation tasks to learning in the embedding space of an autoencoder.
Recommended citation: Florian Mai, Nikolaos Pappas, Ivan Montero, Noah A. Smith and James Henderson. (2020). "Plug and Play Autoencoders for Conditional Text Generation." EMNLP 2020. https://arxiv.org/abs/2010.02983
Published in ICML, 2020
We formulate a benchmarking evaluation protocol that takes the tunability of optimizers into account.
Recommended citation: Prabhu Teja Sivaprasad*, Florian Mai*, Thijs Vogels, Martin Jaggi and Francois Fleuret (2020). "Optimizer Benchmarking Needs to Account for Hyperparameter Tuning." ICML 2020. https://arxiv.org/abs/1910.11758
Published in ACL, 2024
We propose an efficient model for sequence modeling by encoding turn distances in the embedding space.
Recommended citation: Justus-Jonas Erker, Florian Mai, Nils Reimers, Gerasimos Spanakis, and Iryna Gurevych (2024). "Triple-Encoders: Representations That Fire Together, Wire Together." ACL 2024. https://arxiv.org/abs/2402.12332
Published in COLM, 2024
We propose a method to learn planning for language modeling using unlabeled data.
Recommended citation: Nathan Cornille, Marie-Francine Moens, Florian Mai. (2024). "Learning to Plan for Language Modeling from Unlabeled Data." COLM 2024. https://arxiv.org/abs/2404.00614
Published in InterSpeech, 2023
We show that the attention mechanism in the popular Conformer architecture can be replaced with a more efficient alternative at no performance loss.
Recommended citation: Florian Mai, Juan Zuluaga-Gomez, Titouan Parcollet and Petr Motlicek (2023). "HyperConformer: Multi-head HyperMixer for Efficient Speech Recognition." InterSpeech 2023. https://arxiv.org/abs/2305.18281
Published in ACL, 2023
We propose an efficient all-MLP architecture with the same inductive biases as Transformers.
Recommended citation: Florian Mai, Arnaud Pannatier, Fabio Fehr, Haolin Chen, François Marelli, François Fleuret and James Henderson. (2020). "HyperMixer: An MLP-based Low Cost Alternative to Transformers." ACL 2023. https://arxiv.org/abs/2203.03691
Published in NeurIPS, 2023
We propose an new neural architecture for Neural Combinatorial Optimization based on bisimulation quotienting of a CO problem’s MDP.
Recommended citation: Darko Drakulic, Sofia Michel, Florian Mai, Arnaud Sors, Jean-Marc Andreoli (2023). "BQ-NCO: Bisimulation Quotienting for Generalizable Neural Combinatorial Optimization." NeurIPS 2023. https://arxiv.org/abs/2301.03313
Published in AACL, 2022
We embed text into a variable-size embedding space via an autoencoder and propose a method to learn unsupervised conditional text generation tasks in that space.
Recommended citation: Florian Mai and James Henderson. (2022). "Bag-of-Vectors Autoencoders for Unsupervised Conditional Text Generation." AACL 2022. https://arxiv.org/abs/2110.07002
Published in EMNLP, 2020
We reduce conditional text generation tasks to learning in the embedding space of an autoencoder.
Recommended citation: Florian Mai, Nikolaos Pappas, Ivan Montero, Noah A. Smith and James Henderson. (2020). "Plug and Play Autoencoders for Conditional Text Generation." EMNLP 2020. https://arxiv.org/abs/2010.02983
Published in ICML, 2020
We formulate a benchmarking evaluation protocol that takes the tunability of optimizers into account.
Recommended citation: Prabhu Teja Sivaprasad*, Florian Mai*, Thijs Vogels, Martin Jaggi and Francois Fleuret (2020). "Optimizer Benchmarking Needs to Account for Hyperparameter Tuning." ICML 2020. https://arxiv.org/abs/1910.11758
Published in ICLR, 2019
We represent word embeddings as matrices and compose them via matrix multiplication.
Recommended citation: Florian Mai, Lukas Galke and Ansgar Scherp (2019). "CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model." ICLR 2019. https://arxiv.org/abs/1902.06423
Published in RecSys Challenge, 2018
Recommended citation: Iacopo Vagliano, Lukas Galke, Florian Mai and Ansgar Scherp. (2018). "Using Adversarial Autoencoders for Multi-Modal Automatic Playlist Continuation." RecSys 2018. https://dl.acm.org/doi/abs/10.1145/3267471.3267476
Published in UMAP, 2018
We show that adversarial autoencoders perform well at multi-modal recommendation tasks.
Recommended citation: Lukas Galke, Florian Mai, Iacopo Vagliano and Ansgar Scherp. (2018). "Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels." UMAP 2018. https://arxiv.org/abs/1907.12366
Published in JCDL, 2018
We show that title-based text classification can outperform classification based on the full text due to the larger number of available training data.
Recommended citation: Florian Mai, Lukas Galke and Ansgar Scherp. (2018). "Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text." JCDL 2018. https://arxiv.org/abs/1801.06717
Published in KCAP, 2017
We compare different machine learning approaches for text classification from two different modalities, titles and texts.
Recommended citation: Lukas Galke, Florian Mai, Alan Schelten, Dennis Brunsch and Ansgar Scherp. (2017). "Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text." KCAP 2017. https://arxiv.org/abs/1705.05311
Published in INFORMATIK, 2017
Recommended citation: Ahmed Saleh, Florian Mai, Chifumi Nishioka and Ansgar Scherp. (2017). "Reranking-based Recommender System with Deep Learning." INFORMATIK 2017.
Published in arXiv, 2024
We present SpeechBrain 1.0, an open-source toolkit for speech and language processing.
Recommended citation: Mirco Ravanelli, Titouan Parcollet, Adel Moumen, Sylvain de Langen, Cem Subakan, Peter Plantinga, Yingzhi Wang, Pooneh Mousavi, Luca Della Libera, Artem Ploujnikov, Francesco Paissan, Davide Borra, Salah Zaiem, Zeyu Zhao, Shucong Zhang, Georgios Karakasidis, Sung-Lin Yeh, Pierre Champion, Aku Rouhe, Rudolf Braun, Florian Mai, Juan Zuluaga-Gomez, Seyed Mahed Mousavi, Andreas Nautsch, Xuechen Liu, Sangeet Sagar, Jarod Duret, Salima Mdhaffar, Gaelle Laperriere, Mickael Rouvier, Renato De Mori, Yannick Esteve. (2024). "Open-Source Conversational AI with SpeechBrain 1.0." arXiv. https://arxiv.org/abs/2407.00463
Published in OpenReview, 2019
Our natural language inference model learns sentence embeddings that are interpretable in terms of entailment relations.
Recommended citation: Rabeeh Karimi Mahabadi*, Florian Mai* and James Henderson (2019). "Learning Entailment-Based Sentence Embeddings from Natural Language Inference." OpenReview. https://openreview.net/forum?id=BkxackSKvH¬eId=SmKtJ-nJHH