22 citations · 76 across the 10 of their papers we have counts for
10 papers
HyperConformer: Multi-head HyperMixer for Efficient Speech Recognition
Florian Mai, Juan Zuluaga-Gomez, Titouan Parcollet +1
State-of-the-art ASR systems have achieved promising results by modeling local and global interactions separately. While the former can be computed efficiently, global interactions…
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization
Darko Drakulic, Sofia Michel, Florian Mai +2
Despite the success of neural-based combinatorial optimization methods for end-to-end heuristic learning, out-of-distribution generalization remains a challenge. In this paper, we…
HyperMixer: An MLP-based Low Cost Alternative to Transformers
Florian Mai, Arnaud Pannatier, Fabio Fehr +4
Transformer-based architectures are the model of choice for natural language understanding, but they come at a significant cost, as they have quadratic complexity in the input leng…
Bag-of-Vectors Autoencoders for Unsupervised Conditional Text Generation
Florian Mai, James Henderson
Text autoencoders are often used for unsupervised conditional text generation by applying mappings in the latent space to change attributes to the desired values. Recently, Mai et…
Plug and Play Autoencoders for Conditional Text Generation
Florian Mai, Nikolaos Pappas, Ivan Montero +2
Text autoencoders are commonly used for conditional generation tasks such as style transfer. We propose methods which are plug and play, where any pretrained autoencoder can be use…
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels +2
The performance of optimizers, particularly in deep learning, depends considerably on their chosen hyperparameter configuration. The efficacy of optimizers is often studied under n…
Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels
Lukas Galke, Florian Mai, Iacopo Vagliano +1
We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze th…
CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model
Florian Mai, Lukas Galke, Ansgar Scherp
Continuous Bag of Words (CBOW) is a powerful text embedding method. Due to its strong capabilities to encode word content, CBOW embeddings perform well on a wide range of downstrea…
Using Deep Learning for Title-Based Semantic Subject Indexing to Reach Competitive Performance to Full-Text
Florian Mai, Lukas Galke, Ansgar Scherp
For (semi-)automated subject indexing systems in digital libraries, it is often more practical to use metadata such as the title of a publication instead of the full-text or the ab…
Using Titles vs. Full-text as Source for Automated Semantic Document Annotation
Lukas Galke, Florian Mai, Alan Schelten +2
A significant part of the largest Knowledge Graph today, the Linked Open Data cloud, consists of metadata about documents such as publications, news reports, and other media articl…