43 citations · 107 across the 5 of their papers we have counts for
11 papers
Learning to Merge Tokens in Vision Transformers
Cedric Renggli, André Susano Pinto, Neil Houlsby +3
Transformers are widely applied to solve natural language understanding and computer vision tasks. While scaling up these architectures leads to improved performance, it often come…
Evaluating Bayes Error Estimators on Real-World Datasets with FeeBee
Cedric Renggli, Luka Rimanic, Nora Hollenstein +1
The Bayes error rate (BER) is a fundamental concept in machine learning that quantifies the best possible accuracy any classifier can achieve on a fixed probability distribution. D…
A Data Quality-Driven View of MLOps
Cedric Renggli, Luka Rimanic, Nezihe Merve Gürel +3
Developing machine learning models can be seen as a process similar to the one established for traditional software development. A key difference between the two lies in the strong…
Decoding EEG Brain Activity for Multi-Modal Natural Language Processing
Nora Hollenstein, Cedric Renggli, Benjamin Glaus +4
Until recently, human behavioral data from reading has mainly been of interest to researchers to understand human cognition. However, these human language processing signals can al…
On Convergence of Nearest Neighbor Classifiers over Feature Transformations
Luka Rimanic, Cedric Renggli, Bo Li +1
The k-Nearest Neighbors (kNN) classifier is a fundamental non-parametric machine learning algorithm. However, it is well known that it suffers from the curse of dimensionality, whi…
Scalable Transfer Learning with Expert Models
Joan Puigcerver, Carlos Riquelme, Basil Mustafa +5
Transfer of pre-trained representations can improve sample efficiency and reduce computational requirements for new tasks. However, representations used for transfer are usually ge…