1 citations · 1 across the 2 of their papers we have counts for
4 papers
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting
Nouha Karaouli, Denis Coquenet, Elisa Fromont +2
While Time Series Foundation Models (TSFMs) excel in zero-shot tasks, their behavior under continual fine tuning is poorly understood. We present the first systematic study of cata…
N-gram Injection into Transformers for Dynamic Language Model Adaptation in Handwritten Text Recognition
Florent Meyer, Laurent Guichard, Yann Soullard +4
Transformer-based encoder-decoder networks have recently achieved impressive results in handwritten text recognition, partly thanks to their auto-regressive decoder which implicitl…
How Foundational are Foundation Models for Time Series Forecasting?
Nouha Karaouli, Denis Coquenet, Elisa Fromont +2
Foundation Models are designed to serve as versatile embedding machines, with strong zero shot capabilities and superior generalization performance when fine-tuned on diverse downs…
Relaxed syntax modeling in Transformers for future-proof license plate recognition
Florent Meyer, Laurent Guichard, Denis Coquenet +3
Effective license plate recognition systems are required to be resilient to constant change, as new license plates are released into traffic daily. While Transformer-based networks…