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ORCIDFlorian Mai
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2020 – today
- 2024
[c14]Justus-Jonas Erker, Florian Mai, Nils Reimers, Gerasimos Spanakis, Iryna Gurevych:
Triple-Encoders: Representations That Fire Together, Wire Together. ACL (1) 2024: 5317-5332- [i15]Justus-Jonas Erker, Florian Mai, Nils Reimers, Gerasimos Spanakis, Iryna Gurevych:
Triple-Encoders: Representations That Fire Together, Wire Together. CoRR abs/2402.12332 (2024) - [i14]Nathan Cornille, Marie-Francine Moens, Florian Mai:
Learning to Plan for Language Modeling from Unlabeled Data. CoRR abs/2404.00614 (2024) - [i13]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, Gaëlle Laperrière, Mickael Rouvier, Renato De Mori, Yannick Estève:
Open-Source Conversational AI with SpeechBrain 1.0. CoRR abs/2407.00463 (2024) - [i12]Florian Mai, Nathan Cornille, Marie-Francine Moens:
Learning to Plan Long-Term for Language Modeling. CoRR abs/2409.00070 (2024) - [i11]Nathan Cornille, Florian Mai, Jingyuan Sun, Marie-Francine Moens:
End-to-end Planner Training for Language Modeling. CoRR abs/2410.12492 (2024) - 2023
- [c13]Florian Mai, Arnaud Pannatier, Fabio Fehr, Haolin Chen, François Marelli
, François Fleuret, James Henderson:
HyperMixer: An MLP-based Low Cost Alternative to Transformers. ACL (1) 2023: 15632-15654 - [c12]Florian Mai, Juan Zuluaga-Gomez, Titouan Parcollet, Petr Motlícek:
HyperConformer: Multi-head HyperMixer for Efficient Speech Recognition. INTERSPEECH 2023: 2213-2217 - [c11]Darko Drakulic, Sofia Michel, Florian Mai, Arnaud Sors, Jean-Marc Andreoli:
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization. NeurIPS 2023 - [i10]Darko Drakulic, Sofia Michel, Florian Mai, Arnaud Sors, Jean-Marc Andreoli:
BQ-NCO: Bisimulation Quotienting for Generalizable Neural Combinatorial Optimization. CoRR abs/2301.03313 (2023) - [i9]Florian Mai, Juan Zuluaga-Gomez, Titouan Parcollet, Petr Motlícek:
HyperConformer: Multi-head HyperMixer for Efficient Speech Recognition. CoRR abs/2305.18281 (2023) - 2022
- [c10]Florian Mai, James Henderson:
Bag-of-Vectors Autoencoders for Unsupervised Conditional Text Generation. AACL/IJCNLP (1) 2022: 468-488 - [i8]Florian Mai, Arnaud Pannatier, Fabio Fehr, Haolin Chen, François Marelli, François Fleuret, James Henderson:
HyperMixer: An MLP-based Green AI Alternative to Transformers. CoRR abs/2203.03691 (2022) - 2021
- [i7]Florian Mai, James Henderson:
Bag-of-Vectors Autoencoders for Unsupervised Conditional Text Generation. CoRR abs/2110.07002 (2021) - 2020
- [c9]Florian Mai, Nikolaos Pappas, Ivan Montero, Noah A. Smith, James Henderson:
Plug and Play Autoencoders for Conditional Text Generation. EMNLP (1) 2020: 6076-6092 - [c8]Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret:
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning. ICML 2020: 9036-9045 - [d1]Florian Mai, Lukas Galke, Ansgar Scherp:
Embeddings from "CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix-Space Model". Zenodo, 2020
- [i6]Florian Mai, Nikolaos Pappas, Ivan Montero, Noah A. Smith, James Henderson:
Plug and Play Autoencoders for Conditional Text Generation. CoRR abs/2010.02983 (2020)
2010 – 2019
- 2019
- [c7]Lukas Galke, Florian Mai, Ansgar Scherp:
What If We Encoded Words as Matrices and Used Matrix Multiplication as Composition Function? GI-Jahrestagung 2019: 287-288 - [c6]Florian Mai, Lukas Galke, Ansgar Scherp:
CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model. ICLR (Poster) 2019 - [i5]Florian Mai, Lukas Galke, Ansgar Scherp:
CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model. CoRR abs/1902.06423 (2019) - [i4]Lukas Galke, Florian Mai, Iacopo Vagliano, Ansgar Scherp:
Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels. CoRR abs/1907.12366 (2019) - [i3]Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret:
On the Tunability of Optimizers in Deep Learning. CoRR abs/1910.11758 (2019) - 2018
- [c5]Florian Mai, Lukas Galke, Ansgar Scherp:
Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text. JCDL 2018: 169-178 - [c4]Iacopo Vagliano, Lukas Galke, Florian Mai, Ansgar Scherp:
Using Adversarial Autoencoders for Multi-Modal Automatic Playlist Continuation. RecSys Challenge 2018: 5:1-5:6 - [c3]Lukas Galke, Florian Mai, Iacopo Vagliano, Ansgar Scherp:
Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels. UMAP 2018: 197-205 - [i2]Florian Mai, Lukas Galke, Ansgar Scherp:
Using Deep Learning For Title-Based Semantic Subject Indexing To Reach Competitive Performance to Full-Text. CoRR abs/1801.06717 (2018) - 2017
- [c2]Ahmed Saleh, Florian Mai, Chifumi Nishioka, Ansgar Scherp:
Reranking-based Recommender System with Deep Learning. GI-Jahrestagung 2017: 2169-2175 - [c1]Lukas Galke, Florian Mai, Alan Schelten, Dennis Brunsch, Ansgar Scherp:
Using Titles vs. Full-text as Source for Automated Semantic Document Annotation. K-CAP 2017: 20:1-20:4 - [i1]Lukas Galke, Florian Mai, Alan Schelten, Dennis Brunsch, Ansgar Scherp:
Comparing Titles vs. Full-text for Multi-Label Classification of Scientific Papers and News Articles. CoRR abs/1705.05311 (2017)
Coauthor Index
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last updated on 2024-11-25 22:40 CET by the dblp team
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