Search Results for author:
Found 25 papers, 12 papers with code
MedDialog: Large-scale Medical Dialogue Datasets
We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks.
Training Agents with Weakly Supervised Feedback from Large Language Models
Large Language Models (LLMs) offer a promising basis for creating agents that can tackle complex tasks through iterative environmental interaction.
Edge-Enhanced Dilated Residual Attention Network for Multimodal Medical Image Fusion
Multimodal medical image fusion is a crucial task that combines complementary information from different imaging modalities into a unified representation, thereby enhancing diagnostic accuracy and treatment planning.
Towards Democratizing Multilingual Large Language Models For Medicine Through A Two-Stage Instruction Fine-tuning Approach
Open-source, multilingual medical large language models (LLMs) have the potential to serve linguistically diverse populations across different regions.
A Labeled Ophthalmic Ultrasound Dataset with Medical Report Generation Based on Cross-modal Deep Learning
It collects three modal data, including the ultrasound images, blood flow information and examination reports from 2, 417 patients at an ophthalmology hospital in Shenyang, China, during the year 2018, in which the patient information is de-identified for privacy protection.
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks
Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes.
Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification
In this work, we present a novel approach for unpaired image-to-image translation of prostate multi-parametric MRIs and an uncertainty-aware training approach for classifying clinically significant PCa, to be applied in data-constrained settings such as local and small clinics.
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine Reader
We present Pre-trained Machine Reader (PMR), a novel method for retrofitting pre-trained masked language models (MLMs) to pre-trained machine reading comprehension (MRC) models without acquiring labeled data.
An Attention-based Multi-Scale Feature Learning Network for Multimodal Medical Image Fusion
Furthermore, we propose a novel fixed fusion strategy termed Softmax-based weighted strategy based on the Softmax weights and matrix nuclear norm.
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs
Based on the proposed metrics, we analyze two typical phenomena of the change of the transformation complexity during the training process, and explore the ceiling of a DNN's complexity.
Truncated LinUCB for Stochastic Linear Bandits
This paper considers contextual bandits with a finite number of arms, where the contexts are independent and identically distributed $d$-dimensional random vectors, and the expected rewards are linear in both the arm parameters and contexts.
Enhancing Cross-lingual Prompting with Dual Prompt Augmentation
Prompting shows promising results in few-shot scenarios.
Heuristic Hyperparameter Optimization for Convolutional Neural Networks using Genetic Algorithm
In the past few years, deep learning has proven to be one of the most powerful methods in the field of image classification.
Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness
This paper provides a unified view to explain different adversarial attacks and defense methods, i. e. the view of multi-order interactions between input variables of DNNs.
A Unified Game-Theoretic Interpretation of Adversarial Robustness
This paper provides a unified view to explain different adversarial attacks and defense methods, \emph{i. e.} the view of multi-order interactions between input variables of DNNs.
On the Generation of Medical Dialogs for COVID-19
Training complex dialog generation models on small datasets bears high risk of overfitting.
Shoulder Implant X-Ray Manufacturer Classification: Exploring with Vision Transformer
In this paper, we will demonstrate different methods for classifying the manufacturer of a shoulder implant.
A Unified Game-Theoretic Interpretation of Adversarial Robustness
This paper provides a unified view to explain different adversarial attacks and defense methods, i. e. the view of multi-order interactions between input variables of DNNs.
Self-supervised Regularization for Text Classification
The SSL task is unsupervised, which is defined purely on input texts without using any human-provided labels.
Understanding, Analyzing, and Optimizing the Complexity of Deep Models
Based on the proposed metrics, we analyze two typical phenomena of the change of the transformation complexity during the training process, and explore the ceiling of a DNN’s complexity.
Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning
A mobile app interface usually consists of a set of user interface modules.
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
We present a multi-agent actor-critic method that aims to implicitly address the credit assignment problem under fully cooperative settings.
CERT: Contrastive Self-supervised Learning for Language Understanding
We evaluate CERT on 11 natural language understanding tasks in the GLUE benchmark where CERT outperforms BERT on 7 tasks, achieves the same performance as BERT on 2 tasks, and performs worse than BERT on 2 tasks.
MedDialog: Two Large-scale Medical Dialogue Datasets
Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs.
Towards Understanding Chinese Checkers with Heuristics, Monte Carlo Tree Search, and Deep Reinforcement Learning
The game of Chinese Checkers is a challenging traditional board game of perfect information that differs from other traditional games in two main aspects: first, unlike Chess, all checkers remain indefinitely in the game and hence the branching factor of the search tree does not decrease as the game progresses; second, unlike Go, there are also no upper bounds on the depth of the search tree since repetitions and backward movements are allowed.
