Search Results for author:
Found 23 papers, 6 papers with code
Transformer-based Models for Long Document Summarisation in Financial Domain
Summarisation of long financial documents is a challenging task due to the lack of large-scale datasets and the need for domain knowledge experts to create human-written summaries.
FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image Classification
In the second phase, we formulated the classification task to a Federated Transfer Learning (FTL) scheme and proposed a Fine-Tuned Aggregation strategy for Federated Learning (FTA-FTL).
Adapter-Enhanced Semantic Prompting for Continual Learning
Continual learning (CL) enables models to adapt to evolving data streams.
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
We implement DapperFL on a realworld FL platform with heterogeneous clients.
PersoBench: Benchmarking Personalized Response Generation in Large Language Models
We assess the performance of three open-source and three closed-source LLMs using well-known datasets and a range of metrics.
Natural Language-Oriented Programming (NLOP): Towards Democratizing Software Creation
As generative Artificial Intelligence (AI) technologies evolve, they offer unprecedented potential to automate and enhance various tasks, including coding.
Graph Neural Networks for Brain Graph Learning: A Survey
Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders.
RETTA: Retrieval-Enhanced Test-Time Adaptation for Zero-Shot Video Captioning
Specifically, we bridge video and text using four key models: a general video-text retrieval model XCLIP, a general image-text matching model CLIP, a text alignment model AnglE, and a text generation model GPT-2, due to their source-code availability.
When Eye-Tracking Meets Machine Learning: A Systematic Review on Applications in Medical Image Analysis
Eye-gaze tracking research offers significant promise in enhancing various healthcare-related tasks, above all in medical image analysis and interpretation.
StyleDubber: Towards Multi-Scale Style Learning for Movie Dubbing
Given a script, the challenge in Movie Dubbing (Visual Voice Cloning, V2C) is to generate speech that aligns well with the video in both time and emotion, based on the tone of a reference audio track.
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing
FedLPS leverages principles from transfer learning to facilitate the deployment of multiple tasks on a single device by dividing the local model into a shareable encoder and task-specific encoders.
Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023
Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form.
A Personalized Reinforcement Learning Summarization Service for Learning Structure from Unstructured Data
The exponential growth of textual data has created a crucial need for tools that assist users in extracting meaningful insights.
OptIForest: Optimal Isolation Forest for Anomaly Detection
Extensive experiments on a series of benchmarking datasets for comparative and ablation studies demonstrate that our approach can efficiently and robustly achieve better detection performance in general than the state-of-the-arts including the deep learning based methods.
ProcessGPT: Transforming Business Process Management with Generative Artificial Intelligence
We introduce ProcessGPT as a new technology that has the potential to enhance decision-making in data-centric and knowledge-intensive processes.
A Comprehensive Survey on Graph Summarization with Graph Neural Networks
Hence, this paper presents a comprehensive survey of progress in deep learning summarization techniques that rely on graph neural networks (GNNs).
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence
The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing.
DAGAD: Data Augmentation for Graph Anomaly Detection
To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes.
Deep Learning in Spatially Resolved Transcriptomics: A Comprehensive Technical View
In this paper, we provide a comprehensive overview of these deep learning methods, including their strengths and limitations.
Deep reinforcement learning guided graph neural networks for brain network analysis
Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome.
A Survey on Deep Learning Event Extraction: Approaches and Applications
Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.
A Query Language for Summarizing and Analyzing Business Process Data
In the context of business processes, we consider the Big Data problem as a massive number of interconnected data islands from personal, shared and business data.
Enabling the Analysis of Personality Aspects in Recommender Systems
Existing Recommender Systems mainly focus on exploiting users' feedback, e. g., ratings, and reviews on common items to detect similar users.
