output
20182026
most citedMultimodal Few-Shot Learning with Frozen Language Models

86 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

8 papers

eess.IV2026

Impact of Synthetic Lesional MR Images in Automated Focal Cortical Dysplasia Detection in Low-Data Scenarios

Prabhjot Kaur, Hakim Ouaalam, Sedat Kandemirli +2

Background and Purpose: Automated detection of focal cortical dysplasia (FCD) requires large volumes of voxelwise lesion-delineated MRI data, which are difficult to acquire. This s…

cs.LG2025

Towards symbolic regression for interpretable clinical decision scores

Guilherme Seidyo Imai Aldeia, Joseph D. Romano, Fabricio Olivetti de Franca +2

Medical decision-making makes frequent use of algorithms that combine risk equations with rules, providing clear and standardized treatment pathways. Symbolic regression (SR) tradi…

cs.RO2025★ 1 cited

Soft Robotic Delivery of Coiled Anchors for Cardiac Interventions

Leonardo Zamora Yanez, Jacob Rogatinsky, Dominic Recco +4

Trans-catheter cardiac intervention has become an increasingly available option for high-risk patients without the complications of open heart surgery. However, current catheterbas…

cs.CL2025

Using tournaments to calculate AUROC for zero-shot classification with LLMs

WonJin Yoon, Ian Bulovic, Timothy A. Miller

Large language models perform surprisingly well on many zero-shot classification tasks, but are difficult to fairly compare to supervised classifiers due to the lack of a modifiabl…

cs.CV2021★ 86 cited

Multimodal Few-Shot Learning with Frozen Language Models

Maria Tsimpoukelli, Jacob Menick, Serkan Cabi +3

When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we p…

cs.CV2021

PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training

Luke Melas-Kyriazi, Arjun K. Manrai

Unsupervised domain adaptation is a promising technique for semantic segmentation and other computer vision tasks for which large-scale data annotation is costly and time-consuming…

cs.CV2018

Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease

Danielle F. Pace, Adrian V. Dalca, Tom Brosch +5

We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a…

cs.LG2018★ 1 cited

Multi-task multiple kernel machines for personalized pain recognition from functional near-infrared spectroscopy brain signals

Daniel Lopez-Martinez, Ke Peng, Sarah C. Steele +3

Currently there is no validated objective measure of pain. Recent neuroimaging studies have explored the feasibility of using functional near-infrared spectroscopy (fNIRS) to measu…