| Date |
Topic/papers |
Recommended reading |
Lecture Slides |
Notes |
| September 03, 2024 |
Overview of Knowledge-infused Learning |
Duality of Data and Knowledge |
Lecture Slides |
|
| September 05, 2024 |
Shades of Knowledge-infused Learning |
Shades of Knowledge-infused Learning |
Lecture Slides |
|
| September 10, 2024 |
Guest Lecture from Kaushik Roy (Intern Bosch and PhD AIISC, South Carolina) |
Knowledge-infused Neurosymbolic AI:Knowledge Graphs for Enhanced Semantics |
Demo Files |
|
| September 12, 2024 |
Reviewing Shades of Knowledge-infused Learning |
Readings: Knowledge-guided Machine Learning |
Slides |
Task: Compare Knowledge-guided Machine Learning with the perspective presented in Duality of Data and Knowledge. |
| September 17, 2024 |
Grounding LLMs: Building a Knowledge Layer atop the Intelligence Layer By Aman Chadha (Amazon Alexa GenAI & Stanford University) |
ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs; Gaussian Adaptive Attention is All You Need: Robust Contextual Representations Across Multiple Modalities |
Lecture Slides |
|
| September 19-26, 2024 |
Semi-Deep Infused Learning |
Readings: Semantics of the black-box: Can knowledge graphs help make deep learning systems more interpretable and explainable? |
Lecture Slides-1 |
Lecture Slides-2 |
| October 01, 2024 |
Class Project Presentations |
|
|
|
| October 03, 2024 |
Semi-Deep Infused Learning - Markov Chain Monte Carlo |
Readings: Semantics of the black-box: Can knowledge graphs help make deep learning systems more interpretable and explainable? |
Lecture Slides |
|
| October 22, 2024 |
Semi-Deep Infused Learning - Variational Autoencoder |
Knowledge Infused Learning (K-IL):
Towards Deep Incorporation of Knowledge in Deep Learning |
Lecture Slides |
|
| October 29, 2024 |
Knowledge Graph Embeddings |
Readings1: KI-BERT
Readings2: K-BERT
|
Lecture Slides |
KGE is the first step towards Deep Knowledge-infused Learning |
| November 21, 2024 |
KGE and Large Language Models |
Readings 1: LLM+KG
Readings 2: Evaluation KGE Calibration
Readings 3: FactKB
Readings 4: QA-GNN using KG
|
Lecture Slides |
Code Implementation |