Colloquium 2020/2021
For a researcher in a diverse and quickly developing area of knowledge such as computer science, it is important to maintain a broad perspective and strive to understand what colleagues in related fields are studying. This requires a platform where specialists can meet and tell each other about their latest findings in a common language. Such a platform is the Colloquium of HSE's Faculty of Computer Science. This platform is a faculty-wide academic seminar designed for teachers and research staff, graduate and undergraduate students, as well as those who are interested in computer science.
Colloquium meetings are held on Tuesdays in the Faculty of Computer Science building at Kochnovsky Proezd, 3, lecture hall 205, 2nd floor.
NB: a somewhat more detailed web page is available in Russian here.
Date: December 7, 16:20
Placе: online
Title: Highly accurate protein structure prediction with AlphaFold
Speaker: Anna Potapenko, DeepMind
Abstract: Predicting a protein’s structure from its primary sequence has been a grand challenge in biology for the past 50 years, holding the promise to bridge the gap between the pace of genomics discovery and resulting structural characterization. In this talk, we will describe work at DeepMind to develop AlphaFold, a new deep learning-based system for structure prediction that achieves high accuracy across a wide range of targets. We demonstrated our system in the 14th biennial Critical Assessment of Protein Structure Prediction (CASP14) across a wide range of difficult targets, where the assessors judged our predictions to be at an accuracy “competitive with experiment” for approximately 2/3rds of proteins. The talk will cover both the underlying machine learning ideas and the implications for biological research.
Colloquium (PDF, 832 Kb)
Date: November 23, 16:20
Placе: online
Title: Software Release anomaly detection in DevOps environment
Speaker: Manuel Mazzara, Innopolis University
Abstract: In this talk, I present current research on the use of Machine Learning to support DevOps automation and continuous releases. Decisions can be machine-assisted, but ultimately human made.
Date: October 26, 16:20
Placе: online
Title: Is the next deep learning disruption in the physical sciences?
Speaker: Max Welling University of Amsterdam
Abstract: A number of fields, most prominently speech, vision and NLP have been disrupted by deep learning technology. A natural question is: "which application areas will follow next?". My prediction is that the physical sciences will experience an unprecedented acceleration by combining the tools of simulation on HPC clusters with the tools of deep learning to improve and accelerate this process. Together, they form a virtuous cycle where simulations create data that feeds into deep learning models which in turn improves the simulations. In a way, this is like building a self-learning computational microscope for the physical sciences. In this talk I will illustrate this using two recent pieces of work from my lab: molecular simulation and PDE solving. In molecular simulation we try to predict molecular properties or digitally synthesize molecules with prescribed properties. We have built a number of equivariant graph neural networks to achieve this. Partial differential equations (PDEs) are the most used mathematical model in natural sciences to describe physical processes. Intriguingly, we find that PDE solvers can be learned from data using graph neural networks as well, which has the added benefit that we can learn a solver that can generalize across PDEs and different boundary conditions. Moreover, it may open the door to ab initio learning of PDEs directly from data.
Colloquium (PDF, 736 Kb)
Date: September 28, 18:10
Placе: online
Title: Positional Embedding in Transformer-based Models
Speaker: Tatiana Likhomanenko (Apple)
Abstract: Transformers have been shown to be highly effective on problems involving sequential modeling, such as in machine translation (MT) and natural language processing (NLP). Following its success on these tasks, the Transformer architecture raised immediate interest in other domains: automatic speech recognition (ASR), music generation, object detection, and finally image recognition and video understanding. Two major components of the Transformer are the attention mechanism and the positional encoding. Without the latter, vanilla attention Transformers are invariant with respect to input tokens permutations (making "cat eats fish" and "fish eats cat" identical to the model). In this talk we will discuss different approaches on how to encode positional information, their pros and cons: absolute and relative, fixed and learnable, 1D and multidimensional, additive and multiplicative, continuous and augmented positional embeddings. We will also focus on how well different positional embeddings generalize to unseen positions for both interpolation and extrapolation tasks.
Colloquium (PDF, 452 Kb)
Date: June 1, 16:20 – 17:40
Placе: online
Title: Computer Vision and Machine Learning for Environmental Monitoring
Speaker: Konrad Schindler, ETH Zurich
Abstract: I will give an overview of our work that uses machine learning to map and monitor environmental variables (like canopy height, ice cover etc.) from satellite and ground-based images, and discuss engineering challenges that arise in environmental applications.
Colloquium (PDF, 1.62 Mb)
Date: May 18, 16:20 – 17:40
Placе: Pokrovsky blvd 11, room R503
Title: Bruhat interval polytopes which are cubes
Speaker: Mikiya Masuda, Osaka City University Advanced Mathematical Institute, HSE University
Abstract: For a pair of permutations with in the Bruhat order, the Bruhat interval polytope
is defined as the convex hull of points associated with permutations
for
. It lies in a permutohedron and is an example of a Coxeter matroid polytope.
The Bruhat interval polytope is the moment polytope of some subvariety of a flag variety called a Richardson variety and it is known that the Richardson variety is a smooth toric variety if and only if
is combinatorially equivalent to a cube.
In this talk, I will explain that a certain family of Bruhat interval polytopes, which are particularly combinatorially equivalent to a cube, determines triangulations of a polygon. It turns out that the Wedderburn-Etherington numbers which count \emph{unordered} binary trees appear in their classification. If time permits, I will discuss another family of Bruhat interval polytopes and their classification, where directed paths, more generally directed Dynkin diagrams appear.
This talk is based on recent joint work with Eunjeong Lee (IBS-CGP) and Seonjeong Park (Jeonju Univ.).
Colloquium (PDF, 1.61 Mb)
Date: April 13, 18:10 – 19:30
Placе: Pokrovsky blvd 11, room R503
Title: Genetic computers
Speaker: Alan Herbert, InsideOutBio (President and Founder), HSE University (Academic Supervisor of the International Laboratory of Bioinformatics)
Abstract:
• Biologists and Engineers view Programming Problems very Differently
• Encoding of Genetic Information is by both Nucleic Acid Structure (flipons) and Sequence (codons)
• Encoding Genetic Programs by Structure is More Adaptive than by Sequence
• I will use the Biology of Left-handed Z-DNA and Z-RNA to Exemplify these Principles
• I will also Describe the Potential Use of Other Flipon Classes in Genetic Programming
Colloquium (PDF, 235 Kb)
Date: March 2, 16:20 – 17:40
Placе: Pokrovsky blvd 11, room R503
Title: Neural entity linking
Speaker: Alexander Panchenko, Skoltech
Abstract:
In this talk, I will provide a brief survey of recent neural entity linking (EL) systems developed since 2015 as a result of the "deep learning revolution" in NLP. I will try to systemize design features of neural entity linking systems and compare their performances to the best classic methods on the common benchmarks distilling generic architectural components of a neural EL system, like candidate generation and entity ranking summarizing the prominent methods for each of them, such as approaches to mention encoding based on the self-attention architecture.
Besides, various modifications of this general neural entity linking architecture can be grouped by several common themes: joint entity recognition and linking, models for global linking, domain-independent techniques including zero-shot and distant supervision methods, and cross-lingual approaches. Since many neural models take advantage of pre-trained entity embeddings to improve their generalization capabilities, I will also briefly discuss several types of entity embeddings. Finally, we briefly discuss classic applications of entity linking, focusing on the recently emerged use-case of enhancing deep pre-trained masked language models such as BERT. The materials are based on the following survey: Özge Sevgili, Artem Shelmanov, Mikhail Arkhipov, Alexander Panchenko, and Chris Biemann (2021): Neural Entity Linking: A Survey of Models based on Deep Learning. CoRR abs/2006.00575
Colloquium (PDF, 172 Kb)
Presentation (PDF, 2.59 Mb)