Extracting Structured Data from Multi-Modal Input: Neural and Neuro-Symbolic Approaches

Overview

Explore advanced techniques for extracting structured data from multi-modal sources, focusing on neural methods, neuro-symbolic approaches, and LLM-based solutions for processing integrated text and visual elements.

Syllabus

• Introduction to Multi-Modal Data
Overview of Multi-Modal Input Sources
Challenges in Extracting Structured Data from Multi-Modal Inputs
• Neural Methods for Multi-Modal Data
Deep Learning Architectures for Multi-Modal Processing
Convolutional and Recurrent Networks in Image and Text Processing
Transformer Models for Multi-Modal Fusion
• Neuro-Symbolic Approaches
Introduction to Neuro-Symbolic Systems
Integration of Neural Networks and Symbolic Reasoning
Case Studies in Neuro-Symbolic Learning from Multi-Modal Data
• Large Language Models (LLMs) in Multi-Modal Contexts
Functionalities of LLMs like GPT for Multi-Modal Processing
LLM-Based Techniques for Integrating Text and Visual Data
• Extracting Structured Data from Integrated Inputs
Techniques for Data Representation and Fusion
Structured Data Generation from Combined Text and Visual Information
• Applications and Case Studies
Practical Applications: Robotics, Autonomous Vehicles, and Healthcare
Case Studies: Real-World Implementation of Multi-Modal Extraction Techniques
• Ethical and Societal Implications
Bias and Fairness in Multi-Modal Systems
Privacy Concerns in Multi-Modal Data Collection and Processing
• Hands-On Projects
Implementing a Multi-Modal Data Extraction System
Practical Experience with Neural and Neuro-Symbolic Toolkits
• Future Directions in Multi-Modal Data Processing
Emerging Trends and Technologies
Research Frontiers in Neural and Neuro-Symbolic Methods for Multi-Modal Inputs

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Subjects

Data Science