Search Results for author:
Found 20 papers, 5 papers with code
Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in reasoning, planning, and decision-making.
Fine-Tuning Open-Source Large Language Models to Improve Their Performance on Radiation Oncology Tasks: A Feasibility Study to Investigate Their Potential Clinical Applications in Radiation Oncology
Accuracy and ROUGE-1 score were reported for the fine-tuned models and original models.
Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion
This paper addresses the critical need for democratizing large language models (LLM) in the Arab world, a region that has seen slower progress in developing models comparable to state-of-the-art offerings like GPT-4 or ChatGPT 3. 5, due to a predominant focus on mainstream languages (e. g., English and Chinese).
Alignment at Pre-training! Towards Native Alignment for Arabic LLMs
The alignment of large language models (LLMs) is critical for developing effective and safe language models.
Grid-augmented vision: A simple yet effective approach for enhanced spatial understanding in multi-modal agents
Inspired by how humans use grid-based references like chess boards and maps, we propose introducing explicit visual position encoding through a simple grid overlay approach.
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
-Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis.
A MgNO Method for Multiphase Flow in Porous Media
This research investigates the application of Multigrid Neural Operator (MgNO), a neural operator architecture inspired by multigrid methods, in the simulation for multiphase flow within porous media.
The Radiation Oncology NLP Database
ROND is specifically designed to address this gap in the domain of radiation oncology, a field that offers many opportunities for NLP exploration.
Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: physics enhances generalizability
The noisy probing dose method showed better generalizability in the 6 outlier cases than the ROI-based and beam mask-based methods with 3D Gamma passing rates (for prostate cancer, targets: 89. 32%$\pm$1. 45% vs. 93. 48%$\pm$1. 51% vs. 96. 79%$\pm$0. 83%, OARs: 85. 87%$\pm$1. 73% vs. 91. 15%$\pm$1. 13% vs. 94. 29%$\pm$1. 01%).
Benchmarking a foundation LLM on its ability to re-label structure names in accordance with the AAPM TG-263 report
Conclusions: Given the accuracy of GPT-4 in re-labeling structure names of both target volumes and normal tissues as presented in this work, LLMs are poised to be the preferred method for standardizing structure names in radiation oncology, especially considering the rapid advancements in LLM capabilities that are likely to continue.
AceGPT, Localizing Large Language Models in Arabic
This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
RadOnc-GPT: A Large Language Model for Radiation Oncology
This paper presents RadOnc-GPT, a large language model specialized for radiation oncology through advanced tuning methods.
Segment Anything Model (SAM) for Radiation Oncology
Given that SAM, a model pre-trained purely on natural images, can handle the delineation of OARs from medical images with clinically acceptable accuracy, these results highlight SAM's robust generalization capabilities with consistent accuracy in automatic segmentation for radiotherapy.
Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics
We present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions.
FV-MgNet: Fully Connected V-cycle MgNet for Interpretable Time Series Forecasting
By investigating iterative methods for a constrained linear model, we propose a new class of fully connected V-cycle MgNet for long-term time series forecasting, which is one of the most difficult tasks in forecasting.
Predicting Alzheimer's Disease Using 3DMgNet
Alzheimer's disease (AD) is an irreversible neurode generative disease of the brain. The disease may causes memory loss, difficulty communicating and disorientation.
An Interpretive Constrained Linear Model for ResNet and MgNet
We propose a constrained linear data-feature-mapping model as an interpretable mathematical model for image classification using a convolutional neural network (CNN).
Constrained Linear Data-feature Mapping for Image Classification
In this paper, we propose a constrained linear data-feature mapping model as an interpretable mathematical model for image classification using convolutional neural network (CNN) such as the ResNet.
iRDA Method for Sparse Convolutional Neural Networks
We propose a new approach, known as the iterative regularized dual averaging (iRDA), to improve the efficiency of convolutional neural networks (CNN) by significantly reducing the redundancy of the model without reducing its accuracy.
Make $\ell_1$ Regularization Effective in Training Sparse CNN
Compressed Sensing using $\ell_1$ regularization is among the most powerful and popular sparsification technique in many applications, but why has it not been used to obtain sparse deep learning model such as convolutional neural network (CNN)?
