6 papers
LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series
Alexis Roger, Prateek Humane, Zhenghan Tai +4
Can language-pretrained transformers become effective time-series forecasters, and why? In this paper, we show that cross-modal transfer arises because language pretraining precond…
Image Tiling for High-Resolution Reasoning: Balancing Local Detail with Global Context
Anatole Jacquin de Margerie, Alexis Roger, Irina Rish
Reproducibility remains a cornerstone of scientific progress, yet complex multimodal models often lack transparent implementation details and accessible training infrastructure. In…
Multilingual VLM Training: Adapting an English-Trained VLM to French
Jules Lahmi, Alexis Roger
Artificial intelligence has made great progress in recent years, particularly in the development of Vision--Language Models (VLMs) that understand both visual and textual data. How…
Small Vocabularies, Big Gains: Pretraining and Tokenization in Time Series Models
Alexis Roger, Gwen Legate, Kashif Rasul +2
Tokenization and transfer learning are two critical components in building state of the art time series foundation models for forecasting. In this work, we systematically study the…
CHIRP: A Fine-Grained Benchmark for Open-Ended Response Evaluation in Vision-Language Models
Alexis Roger, Prateek Humane, Daniel Z. Kaplan +7
The proliferation of Vision-Language Models (VLMs) in the past several years calls for rigorous and comprehensive evaluation methods and benchmarks. This work analyzes existing VLM…
Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting
Roland Riachi, Kashif Rasul, Arjun Ashok +5
Recent works have demonstrated the effectiveness of adapting pre-trained language models (LMs) for forecasting time series in the low-data regime. We build upon these findings by a…