research
∙
02/02/2023
Efficient Graph Field Integrators Meet Point Clouds
We present two new classes of algorithms for efficient field integration...
share
research
∙
10/22/2022
Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks
Large pretrained Transformer-based language models like BERT and GPT hav...
research
∙
05/06/2022
Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports
Pretrained Transformer based models finetuned on domain specific corpora...
research
∙
04/09/2022
Efficient Extraction of Pathologies from C-Spine Radiology Reports using Multi-Task Learning
Pretrained Transformer based models finetuned on domain specific corpora...
research
∙
10/08/2021
Hybrid Random Features
We propose a new class of random feature methods for linearizing softmax...
research
∙
09/28/2021
Fine-tuning Vision Transformers for the Prediction of State Variables in Ising Models
Transformers are state-of-the-art deep learning models that are composed...
research
∙
10/12/2020
Permutation invariant networks to learn Wasserstein metrics
Understanding the space of probability measures on a metric space equipp...
research
∙
06/23/2020
Learning Potentials of Quantum Systems using Deep Neural Networks
Machine Learning has wide applications in a broad range of subjects, inc...
research
∙
06/23/2020
Gaining insight into SARS-CoV-2 infection and COVID-19 severity using self-supervised edge features and Graph Neural Networks
Graph Neural Networks (GNN) have been extensively used to extract meanin...
research
∙
02/14/2020
Disease State Prediction From Single-Cell Data Using Graph Attention Networks
Single-cell RNA sequencing (scRNA-seq) has revolutionized biological dis...
research
∙
09/22/2019