| Lec. |
Date |
Topic |
Slides |
Deadlines |
| 1 |
Sept. 10 |
Introduction |
Lecture00, Lecture01 |
|
| 2 |
Sept. 17 |
Convolutional Neural Networks |
Lecture02 |
Indicate two prefered papers due |
| 3 |
Sept. 24 |
Recurrent Neural Networks |
Lecture03 |
Assignment 1 is out! pdf, Code, Tutorial |
| 4 |
Oct. 1 |
Regularization, Batch normalization |
Lecture04 |
|
| One-Shot Learning |
- Siamese Neural Network for One-Shot Image Recognition (Adarsh, slides)
- FaceNet: A Unified Embedding for Face Recognition and Clustering (Shubhank, slides)
|
| 5 |
Oct. 8 |
One-Shot Learning, Graph Neural Networks |
- Semi-Supervised Classification with Graph Convolutional Networks (Abdullah, slides)
- Graph Attention Networks (Abdullah, slides)
- Matching Networks for One Shot Learning (Omid, slides)
- Low Data Drug Discovery with One-Shot Learning (Amirhossein, slides)
- Learning to Remember Rare Events (Geetika, slides)
|
Assignment 1 due |
| 6 |
Oct. 15 |
Domain Adaptation, Multi-task Learning, Distilling the Knowledge, Multiple Instance Learning |
- A Hybrid Instance-based Transfer Learning Method (Nkechinyere, slides)
- Unsupervised Visual Domain Adaptation Using Subspace Alignment (Yingjun, slides)
- Identifying beneficial task relations for multi-task learning in deep neural networks (Isabelle, slides)
- Deep Mutual Learning (Vasileios, slides)
- Multiple Instance Learning: A Survey of Problem Characteristics and Applications (Sedevizo, slides)
|
Project Proposal due.
Assignment 2 is out! pdf(corrected), Code.
|
|
Oct. 22 |
Fall Break |
No class |
|
| 7 |
Oct. 29 |
Zero-Shot Learning |
- A survey of zero-shot learning: Settings, methods, and applications (Michael, slides))
- Attribute-Based Classification for Zero-Shot Visual Object Categorization (Yingjun, slides)
- Unsupervised Domain Adaptation for Zero-Shot Learning (Nkechinyere, slides)
- Label-activating framework for zero-shot learning (Razieh, slides)
|
Assignment 2 due Assignment 2 extended deadline Nov 1 |
| 8 |
Nov. 5 |
Zero-Shot Learning |
- Zero-shot learning via joint latent similarity embedding (Razieh, slides)
- Latent Embeddings for Zero-shot Classification (Sedevizo, slides)
- Semantic Autoencoder for Zero-Shot Learning (Vasileios, slides)
- Relative Attributes (Mohammad, slides)
|
|
| 9 |
Nov. 12 |
Clustering |
- A Co-training Approach for Multi-view Spectral Clustering (Yousef, slides)
- A survey of clustering with deep learning: From the perspective of network architecture (Adam, slides)
- Unsupervised deep embedding for clustering analysis (Adarsh, slides)
- Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering (Adam, slides)
|
|
| 10 |
Nov. 19 |
Interpretable AI |
- Why Should I Trust You?: Explaining the Predictions of Any Classifier (Diana, slides)
- A Unified Approach to Interpreting Model Predictions (Isabelle, slides)
- Why Is My Classifier Discriminatory? (Geetika, slides)
- Towards A Rigorous Science of Interpretable Machine Learning (Diana, slides)
- Attention is not Explanation (Jacob, slides)
|
|
| 11 |
Nov. 26 |
Interpretable AI |
- Anchors: High-Precision Model-Agnostic Explanations (Omid, slides)
- Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives (Mayank, slides)
- Examples are not enough, learn to criticize! criticism for interpretability (Yousef, slides)
- Interpretability beyond feature attribution: Quantitative Testing with Concept Activation Vectors (Mohammad, slides)
|
|
| 12 |
Dec. 3 |
Project Presentations |
|
|
|
|
|
|
Project reports due on Dec. 14 |