YoVDO

Divide and Conquer - Concept-based Models for Efficient Transfer Learning

Offered By: Stanford University via YouTube

Tags

Transfer Learning Courses Neural Networks Courses Radiology Courses Medical Imaging Courses First-Order Logic Courses Domain Adaptation Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a one-hour conference talk by Shantanu Ghosh from Stanford University on developing concept-based interpretable models for efficient transfer learning in healthcare AI. Dive into the challenges of building generalizable AI models for medical imaging and learn about a novel approach that combines blackbox neural networks with interpretable components. Discover how this method iteratively carves out concept-based models using First Order Logic, potentially improving generalizability and reducing the need for extensive labeled data in target domains. Gain insights into the speaker's research on blurring the distinction between post-hoc explanations and interpretable model construction, and understand the implications for enhancing AI model flexibility, explainability, and transfer efficiency in medical applications.

Syllabus

MedAI #126: Divide & Conquer - Concept-based Models for Efficient Transfer Learning | Shantanu Ghosh


Taught by

Stanford MedAI

Tags

Related Courses

Neural Networks for Machine Learning
University of Toronto via Coursera
Good Brain, Bad Brain: Basics
University of Birmingham via FutureLearn
Statistical Learning with R
Stanford University via edX
Machine Learning 1—Supervised Learning
Brown University via Udacity
Fundamentals of Neuroscience, Part 2: Neurons and Networks
Harvard University via edX