13 citations · 33 across the 24 of their papers we have counts for
7 papers · 1 filter
FastVLM: Efficient Vision Encoding for Vision Language Models
Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li +8
Scaling the input image resolution is essential for enhancing the performance of Vision Language Models (VLMs), particularly in text-rich image understanding tasks. However, popula…
Promoting cross-modal representations to improve multimodal foundation models for physiological signals
Ching Fang, Christopher Sandino, Behrooz Mahasseni +5
Many healthcare applications are inherently multimodal, involving several physiological signals. As sensors for these signals become more common, improving machine learning methods…
Generalizable autoregressive modeling of time series through functional narratives
Ran Liu, Wenrui Ma, Ellen Zippi +8
Time series data are inherently functions of time, yet current transformers often learn time series by modeling them as mere concatenations of time periods, overlooking their funct…
MUSCLE: A Model Update Strategy for Compatible LLM Evolution
Jessica Echterhoff, Fartash Faghri, Raviteja Vemulapalli +4
Large Language Models (LLMs) are regularly updated to enhance performance, typically through changes in data or architecture. Within the update process, developers often prioritize…
DataComp-LM: In search of the next generation of training sets for language models
Jeffrey Li, Alex Fang, Georgios Smyrnis +56
We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models. As part of DCLM, we provide a standardize…
CLIP with Quality Captions: A Strong Pretraining for Vision Tasks
Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri +1
CLIP models perform remarkably well on zero-shot classification and retrieval tasks. But recent studies have shown that learnt representations in CLIP are not well suited for dense…
Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum
Hadi Pouransari, Chun-Liang Li, Jen-Hao Rick Chang +4
Large language models (LLMs) are commonly trained on datasets consisting of fixed-length token sequences. These datasets are created by randomly concatenating documents of various…