PR/CV Reading group
Please note that the schedule for the first cycle is very tentative, since there was little consensus over the type of papers that people wanted to discuss. Let's take a part of the first meeting to discuss what everyone would like to get out of these meetings.
You can find the Google Calendar for these meetings and the CoffeeTalks here
Cycle 1 (Spring 2014): Deep Learning
Recently learned deep architectures, such as convolutional neural networks, deep boltzman machines and deep belief networks, have shown large performance improvements over the state of the art on many prediction tasks. In this cycle we will investigate how these methods work and how and when to apply them in practice.
Date | Location | Paper | Download |
7 March 2014 | HB13.100 | Hastie et. al. - Elements of Statistical Learning: Chapter 11: Neural Networks | Link |
21 March 2014 | HB13.100 | Bengio et. al - Representation Learning: A Review and New Perspectives - Sections 1-2, 5, 6.2, 7-7.2.3, 8 | Link |
4 + 11 April 2014 | HB13.100 | Bengio et. al - Representation Learning: A Review and New Perspectives - Sections 3, 4, 10.1, 11-11.2, 12 | Link |
2 May 2014 | HB13.100 | Krizhevsky et. al. - ImageNet Classification with Deep Convolutional Neural Networks | Link |
9 May 2014 | HB13.100 | Postponed | |
23 May 2014 | HB13.100 | Taigman et. al - DeepFace: Closing the Gap to Human-Level Performance in Face Verification | Link |
12 June 2014 | HB13.100 | Zeiler & Fergus - Visualizing and Understanding Convolutional Networks Also have a look at this blogpost | Link |
A related CVPR tutorial (link by Wenjie).
Cycle 2 (Fall 2014): Gaussian Processes
Date | Location | Paper | Download |
19 September 2014 | HB13.100 | Rasmussen & Willams - Gaussian Processes for Machine Learning: Chapter 1 up to and including section 2 of Chapter 2 | Link |
3 October 2014 | HB13.100 | Rasmussen & Willams - Gaussian Processes for Machine Learning: Chapter 2.3 up to and including section 3 of Chapter 3 | Link |
17 October 2014 | HB13.100 | Canceled | Link |
31 October 2014 | HB13.100 | Canceled | Link |
14 November 2014 | HB13.100 | Rasmussen & Willams - Gaussian Processes for Machine Learning: Chapter 3 up to and including section 4 of Chapter 3 | Link |
12 December 2014 | HB13.100 | Rasmussen & Willams - Gaussian Processes for Machine Learning: Chapter 4 | Link |
Related slides (link by Andre).
Cycle 3 (Spring 2015): Graphical Models
Date | Location | Paper | Download |
16 January 2015 | HB13.100 | Christopher Bishop - Pattern Recognition and Machine Learning: Chapter 8 up to and including 8.3 | |
6 February 2015 | HB13.100 | Christopher Bishop - Pattern Recognition and Machine Learning: Chapter 8: 8.4 | |
20 February 2015 | HB13.100 | Christopher Bishop - Pattern Recognition and Machine Learning: Chapter 8: 8.4 (Again) and Dense CRF paper suggested by Gorkem | Link |
6 March 2015 | HB13.100 | Philipp Krahenbuhl & Vladlen Koltun - Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials | Link |
20 March 2015 | HB13.100 | Herbrich, Ralf; Minka, Tom; Graepel, Thore - TrueSkill: A Bayesian Skill Rating System | Link |
27 March 2015 | HB13.100 | Roweis, S. & Ghahramani, Z., 1999. A unifying review of linear gaussian models. Neural computation, 11(1995), pp.305–345. (First 15 pages) |
Cycle 4 (Summer 2015): Graphical Models, Exponential Families and Variational Inference
During the summer (2015) we will be reading the monograph "Graphical Models, Exponential Families and Variational Inference" by Wainwright and Jordan.
Cycle 5 (Fall 2015): Submodularity
Date | Location | Paper | Download |
20 November 2015 13:00 | HB13.100 | Andreas Krause & Daniel Golovin - Submodular Function Maximization pg. 1-9 | Download |
27 November 2015 13:00 | HB13.100 | Andreas Krause & Daniel Golovin - Submodular Function Maximization pg. 10-24 | Download |
11 December 2015 13:00 | HB13.100 | Hoi, S.C.H. et al., 2006. Batch mode active learning and its application to medical image classification. In Proceedings of the 23rd International Conference on Machine learning. pp. 417–424 | Download |
8 January 2016 13:00 | HB13.100 | Lin, H. & Bilmes, J., 2011. A Class of Submodular Functions for Document Summarization. Computational Linguistics, 1, pp.510–520. | Download |
22 January 2016 13:00 | HB13.100 | Narasimhan, M., Jojic, N. & Bilmes, J., 2005. Q-Clustering. In Advances in Neural Information Processing Systems. pp. 979–986. | Download |
12 February 2016 13:00 | HB13.100 | Bach, F., 2010. Structured sparsity-inducing norms through submodular functions. Advances in Neural Information Processing Systems NIPS’2010, pp.1–9. | Download |
Cycle 6 (Spring 2016): Learning bounds for regression and classification
Date | Location | Paper | Download |
26 February 2016 15:00 | HB13.100 | Mohri, Rostamizadeh & Talwalkar - Foundations of Machine Learning: Chapter 2 | Download |
4 March 2016 13:00 | HB13.100 | Mohri, Rostamizadeh & Talwalkar - Foundations of Machine Learning: Chapter 2 | Download |
18 March 2016 13:00 | HB13.100 | Mohri, Rostamizadeh & Talwalkar - Foundations of Machine Learning: Chapter 3 | Download |
21 April 2016 13:00 | HB13.100 | Mohri, Rostamizadeh & Talwalkar - Foundations of Machine Learning: 3.2-3.4 | Download |
13 May 2016 13:00 | HB13.100 | Mohri, Rostamizadeh & Talwalkar - Foundations of Machine Learning: 4.4. Margin-theory, 5.3.3. Learning Guarantees and 10.2. Generalization Bounds | Download |
27 May 2016 13:00 | HB13.100 | Mohri, Rostamizadeh & Talwalkar - Foundations of Machine Learning: 11. Algorithmic Stability | Download |
List of suggested future topics
- Markov Chain Monte Carlo methods
- Gaussian Processes
- Learning bounds for regression and classification
- Manifolds and Riemannian Geometry
- Machine Learning with Privacy Constraints
- Big Data (as suggested by Marco)
Colloquium Slides
Thijs van Ommen - Inconsistency of Bayesian inference when the model is wrong, and how to repair it (slides)