Machine Learning for Computer Vision Institute of AI Faculty of Computer Science TU Dresden

Machine Learning 2 Seminar (Summer Term 2021)



Date Time Seminar Topic
June 1st 09:40 - 10:20 (cancelled)
11:00 - 11:40 Join live Deep Double Descent: Where Bigger Models and More Data Hurt
11:40 - 12:20 (cancelled)
June 2nd 09:00 - 09:40 Join live Consistent k-Clustering for General Metrics
11:40 - 12:20 Join live On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
12:20 - 13:00 Join live Optimizing Rank-Based Metrics With Blackbox Differentiation
15:20 - 16:00 Join live The Linear Ordering Problem. Chapter 6 (Linear Ordering Polytope)
16:00 - 16:40 Join live Neuro-algorithmic Policies enable Fast Combinatorial Generalization
16:40 - 17:20 Join live Towards Understanding the Invertibility of Convolutional Neural Networks
17:20 - 18:00 Join live The Linear Ordering Problem. Chapter 4 (Branch-and-Bound)
June 19th 09:00 - 09:40 Join live Deep Learning using Linear Support Vector Machines
10:20 - 11:00 Join live Implicit Regularization in Deep Matrix Factorization
11:00 - 11:40 Join live Invertible Residual Networks
15:20 - 16:00 Join live The Linear Ordering Problem. Chapter 5 (Branch-and-Cut)
16:40 - 17:20 Join live Exponential expressivity in deep neural networks through transient chaos
June 20th 09:40 - 10:20 Join live Sets Clustering
10:20 - 11:00 Join live Invertible Convolutional Networks
11:00 - 11:40 Join live Totally Deep Support Vector Machines
15:20 - 16:00 Join live The Linear Ordering Polytope
16:00 - 16:40 Join live A Better k-means++ Algorithm via Local Search
16:40 - 17:20 Join live The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
17:20 - 18:00 Join live Current Regularization Techniques for Neural Networks
July 21st 09:40 - 10:20 Join live Graph convolutional networks - Geometric deep learning on graphs and manifolds using mixture model CNNs
10:20 - 11:00 Join live Attention is All you Need
11:00 - 11:40 Join live Structured Prediction with Partial Labelling through the Infimum Loss
11:40 - 12:20 Join live Not All Samples Are Created Equal:Deep Learning with Importance Sampling
12:20 - 13:00 Join live Semi-Supervised Classification with Graph Convolutional Networks
July 22nd 09:00 - 09:40 Join live t-SNE 1: Stochastic neighbor embedding
09:40 - 10:20 Join live t-SNE 2
10:20 - 11:00 Join live Exact Algorithms for the Quadratic Linear Ordering Problem


In this seminar, participating students will read, understand, prepare and present the contents of a research article or book chapter on a topic from the field of machine learning. The article or book chapter will be chosen by the student from the list below, or suggested by the student for approval in the beginning of the term. The preparation will include relevant foundational and related work. By attending at least 10 presentations of their peers, students will get an overview of diverse topics in the field of machine learning.


Prerequisites for taking this course are a strong background in mathematics (esp. linear algebra and analysis) and theoretical computer science, as well as basics of machine learning, comparable to the contents of the course Machine Learning 1. For some of the suggested articles and book chapters, additional knowledge from the field of mathematical optimization is required.


Requirements for passing this course are:


In their preparation, participating students are supervised remotely, by email. They are strongly encouraged to report on their progress briefly, every Friday, by email.

Suggested Research Articles

Unsupervised Learning

Structured Learning (Graphical Models)




Deep Learning (Theory)

Deep Learning (Applied)

Legal notice Privacy