activity
20102024
most citedMLHOps: Machine Learning for Healthcare Operations

6 citations · 16 across the 15 of their papers we have counts for

collaborators

15 papers

cs.CL2024

Graph-tree Fusion Model with Bidirectional Information Propagation for Long Document Classification

Sudipta Singha Roy, Xindi Wang, Robert E. Mercer +1

Long document classification presents challenges in capturing both local and global dependencies due to their extensive content and complex structure. Existing methods often strugg…

cs.SD2024

Self-Supervised Embeddings for Detecting Individual Symptoms of Depression

Sri Harsha Dumpala, Katerina Dikaios, Abraham Nunes +3

Depression, a prevalent mental health disorder impacting millions globally, demands reliable assessment systems. Unlike previous studies that focus solely on either detecting depre…

cs.CL20242 cited

Plug and Play with Prompts: A Prompt Tuning Approach for Controlling Text Generation

Rohan Deepak Ajwani, Zining Zhu, Jonathan Rose +1

Transformer-based Large Language Models (LLMs) have shown exceptional language generation capabilities in response to text-based prompts. However, controlling the direction of gene…

cs.CL2023

A State-Vector Framework for Dataset Effects

Esmat Sahak, Zining Zhu, Frank Rudzicz

The impressive success of recent deep neural network (DNN)-based systems is significantly influenced by the high-quality datasets used in training. However, the effects of the data…

cs.CL2023

Measuring Information in Text Explanations

Zining Zhu, Frank Rudzicz

Text-based explanation is a particularly promising approach in explainable AI, but the evaluation of text explanations is method-dependent. We argue that placing the explanations o…

cs.CV20231 cited

SurGNN: Explainable visual scene understanding and assessment of surgical skill using graph neural networks

Shuja Khalid, Frank Rudzicz

This paper explores how graph neural networks (GNNs) can be used to enhance visual scene understanding and surgical skill assessment. By using GNNs to analyze the complex visual da…