13 citations · 33 across the 24 of their papers we have counts for
9 papers · 1 filter
CPEP: Contrastive Pose-EMG Pre-training Enhances Gesture Generalization on EMG Signals
Wenhui Cui, Christopher Sandino, Hadi Pouransari +7
Hand gesture classification using high-quality structured data such as videos, images, and hand skeletons is a well-explored problem in computer vision. Leveraging low-power, cost-…
Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting
Chen Huang, Skyler Seto, Hadi Pouransari +6
Vision foundation models pre-trained on massive data encode rich representations of real-world concepts, which can be adapted to downstream tasks by fine-tuning. However, fine-tuni…
TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining
Jeffrey Li, Mohammadreza Armandpour, Iman Mirzadeh +8
Large Language Models (LLMs) trained on historical web data inevitably become outdated. We investigate evaluation strategies and update methods for LLMs as new data becomes availab…
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…
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 meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement
Mohammadreza Salehi, Mehrdad Farajtabar, Maxwell Horton +7
Contrastive language image pretraining (CLIP) is a standard method for training vision-language models. While CLIP is scalable, promptable, and robust to distribution shifts on ima…
Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals
Ran Liu, Ellen L. Zippi, Hadi Pouransari +5
Leveraging multimodal information from biosignals is vital for building a comprehensive representation of people's physical and mental states. However, multimodal biosignals often…
APE: Aligning Pretrained Encoders to Quickly Learn Aligned Multimodal Representations
Elan Rosenfeld, Preetum Nakkiran, Hadi Pouransari +2
Recent advances in learning aligned multimodal representations have been primarily driven by training large neural networks on massive, noisy paired-modality datasets. In this work…