You are all cordially invited to the second AMLab seminar this week, on Thursday November 1 at 16:00 in C3.163, where Stephan Alaniz will give a talk titled “Iterative Binary Decision”. Afterwards there are the usual drinks and snacks!
Abstract: The complexity of functions a neural network approximates make
it hard to explain what the classification decision is based on. In this
work, we present a framework that exposes more information about this
decision-making process. Instead of producing a classification in a
single step, our model iteratively makes binary sub-decisions which,
when combined as a whole, ultimately produce the same classification
result while revealing a decision tree as thought process. While there
is generally a trade-off between interpretability and accuracy, the
insights our model generates come at a negligible loss in accuracy. The
decision tree resulting from the sequence of binary decisions of our
model reveal a hierarchical clustering of the data and can be used as
learned attributes in zero-shot learning.
Noud de Kroon has joined the UvA in October 2018 as a PhD student of AMLab, under the joint supervision of dr. Joris Mooij and dr. Danielle Belgrave (Microsoft Research Cambridge). Previously, he obtained a bachelor’s degree in software science at Eindhoven University of Technology and a master’s degree in computer science at the University of Oxford. His research focus is on combining causality and reinforcement learning in order to make better
decisions and improve data efficiency, with applications for example in the medical domain.
For more information about this vacancy, please visit Vacancies
For more information on this vacancy, see Vacancies.
You are all cordially invited to the AMLab seminar at Tuesday June 21 at 16:00 in C3.163, where Matthias Reisser will give a talk titled “Distributed Bayesian Deep Learning”. Afterwards there are the usual drinks and snacks!
Abstract: I would like to give you an overview on what my PhD topic going to be about, as well as present my first project along with initial results: Although deep learning becomes more and more data efficient, it is still true that with more data, more complex models with better generalization capabilities can be trained. More data and bigger models require more computation, resulting in longer training times and slow experiment cycles. One valid approach to speed up computations is by distributing them across machines. At the same time, in the truly huge data regime, as well as for privacy reasons, data may not be accessible from any machine, requiring distributed computations. In a first project, we look at variational inference and a principled approach to distributed training of one joint model. I am looking forward to your opinion and will be grateful for any feedback. Although I am a QUVA member, every UVA-employee is welcome to attend, independent on whether you have signed the QUVA NDA.
You are all cordially invited to the AMLab seminar at Tuesday May 31 at 16:00 in C3.163, where Matthijs Snel from Optiver will give a talk titled “An introduction to market making and data science at Optiver”. Afterwards there are the usual drinks and snacks!
Abstract: Optiver is an electronic market maker with significant presence on equity and derivatives exchanges around the world. Our automated trading strategies operate as semi-autonomous agents, processing information and making multiple decisions in the blink of an eye. In this talk, I will explain some basic market making concepts, supported by real-world examples of market microstructure. I will also provide an overview of what kind of data and challenges our strategies and machine learning applications deal with.