AIML @ Lund University

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AIML@LU Events

AIML@LU WS: AI & ML Technologies

Robot and apple
Robot, apple, hand... Photo: Maj Stenmark


From: 2019-08-30 09:30 to: 15:30
Place: E:A, E-building, Ole Römers väg 3, LTH, Lund University
Contact: Jonas [dot] Wisbrant [at] cs [dot] lth [dot] se
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This AIML@LU fika-to-fika workshop focuses on the development of the technologies that form the basis of Artificial Intelligence and Machine Learning. Possible topics to discuss are the research front for different types of AI, but also to look at different techniques for machine learning.

Room E:A, E-huset, Ole Römers väg 3 LundWhen: 30 August at 9.30 - 15.30  

Where: E:A, E-building, Ole Römers väg 3, LTH, Lund University


9.30 Fika and mingle

10.15 Introduction and update regarding the AIML@LU network

10.30 Ongoing projects

Collaborative reading robotMartin Karlsson, Lund University: Robot Programming by Demonstration Based on Machine Learning

Abstract: Whereas humans would prefer to program on a high level of abstraction, for instance through natural language, robots require very detailed instructions, for instance time series of desired joint torques. In this research, we aim to meet the robots half way, by enabling programming by demonstration.

Marcus Klang, Lund University:  Finding Things in Strings

Najmeh Abiri, Lund University: Variational Autoencoders

Joakim Johnander, Linköping University: Deep Recurrent Neural Networks for Video Object Segmentation

12.00 Lunch, mingle and poster session

13.00 Future trends and interesting examples

Mikael GreebMikael Green, Desupervised2: Bayesian Deep Probabilistic Programming: Are we there yet?

Abstract: Not many would argue against the Bayesian paradigm being the most useful one in modeling problems where parameter estimations are inherently uncertain. But unfortunately most interesting models, especially the ones we know from deep learning, have been very hard to fit in any reasonable amount of time. When dealing with +10 million parameters and +100 thousand data points, Markov Chain Monte Carlo just isn't a viable option. This is why almost every practitioner in deep learning defaults to maximum likelihood estimates through optimization via stochastic gradient descent, because it's much faster. In this talk we'll explore a promising way of doing full Bayesian inference on large scale models via stochastic black box variational inference.

ErikErik Gärtner, Lund University: Intrinsic Motivation - Exploration, curiosity and learning for learning's sake

Abstract: Humans as well as other animals are curious beings that develop cognitive skills on their own without the need for external goals or supervision.
Inspired by this, how can we encourage AIs to learn and solve tasks by themselves?
This talk presents the fascinating area of intrinsic reward in the context of reinforcement learning by showcasing recent articles and results.

14.30 Summary and conclusions

15.00 Fika and mingel



To participate is free of charge, but please register no later than 28 August 12.00 at:


If you have any questions, suggestions or would like to contribute to the program please contact one of:

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