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 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 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 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." arXiv. https://arxiv.org/abs/2402.12332
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