Empirical Inference
Talk
Professor Jürgen Schmidhuber
11-04-2019
Unsupervised Learning: Passiv and Active
I’ll start with a concept of 1990 that has become popular: unsupervised learning without a teacher through two adversarial neural networks (NNs) that duel in a minimax game, where one NN minimizes the objective function maximized by the other. The first NN generates data through its output actions, the second NN predicts the data. The second NN minimizes its error, thus becoming a better predictor. But it is a zero sum game: the first NN tries to find actions that maximize the error of the second NN. The system exhibits what I called “artificial curiosity” because the first NN is motivated ...
Bernhard Schölkopf
Empirical Inference
Talk
Prof. Martin Spindler
11-06-2018
Double Machine Learning with two Applications: Transformation Models and Gaussian Graphical Models in High-Dimensional Settings
In this talk first an introduction to the double machine learning framework is given. This allows inference on parameters in high-dimensional settings. Then, two applications are given, namely transformation models and Gaussian graphical models in high-dimensional settings. Both kind of models are widely used by practitioners. As high-dimensional data sets become more and more available, it is important to allow situations where the number of parameters is large compared to the sample size.
Philipp Geiger
Empirical Inference
Talk
Emily BJ Coffey
14-05-2018
Machine learning in cognitive neuroscience: questions, challenges and potential opportunities
In this talk I will describe the main types of research questions and neuroimaging tools used in my work in human cognitive neuroscience (with foci in audition and sleep), some of the existing approaches used to analyze our data, and their limitations. I will then discuss the main practical obstacles to applying machine learning methods in our field. Several of my ongoing and planned projects include research questions that could be addressed and perhaps considerably extended using machine learning approaches; I will describe some specific datasets and problems, with the goal of exploring i...
Mara Cascianelli
Empirical Inference
Talk
Aljoscha Leonhardt
27-02-2018
A naturalistic perspective on optic flow processing in the fly
Optic flow offers a rich source of information about an organism’s environment. Flies, for instance, are thought to make use of motion vision to control and stabilise their course during acrobatic airborne manoeuvres. How these computations are implemented in neural hardware and how such circuits cope with the visual complexity of natural scenes, however, remain open questions. This talk outlines some of the progress we have made in unraveling the computational substrate underlying optic flow processing in Drosophila. In particular, I will focus on our efforts to connect neural mechanisms a...
Michel Besserve
Empirical Inference
Talk
Robert Peharz
06-12-2017
Sum-Product Networks for Probabilistic Modeling
Probabilistic modeling is the method of choice when it comes to reasoning under uncertainty. However, one of the main practical downsides of probabilistic models is that inference, i.e. the process of using the model to answer statistical queries, is notoriously hard in general. This led to a common folklore that probabilistic models which allow exact inference are necessarily simplistic and undermodel any practical task. In this talk, I will present sum-product networks (SPNs), a recently proposed architecture representing a rich and expressive class of probability distributions, which als...
Empirical Inference
Talk
Ioannis Papantonis
24-07-2017
Adaptive Learning Rate Algorithms for Stochastic Optimization and Variational Bayesian Inference
We present a way to set the step size of Stochastic Gradient Descent, as the solution of
a distance minimization problem. The obtained result has an intuitive interpretation and
resembles the update rules of well known optimization algorithms. Also, asymptotic results
to its relation to the optimal learning rate of Gradient Descent are discussed.
In addition, we talk about two different estimators, with applications in
Variational inference problems, and present approximate results about their variance.
Finally, we combine all of the above, to present an optimization algorithm that...
Philipp Hennig
Empirical Inference
Talk
Prof. Stéphanie Lacour
11-07-2017
Soft bioelectronics: Materials and Technology
Bioelectronics integrates principles of electrical engineering and materials science to biology, medicine and ultimately health. Soft bioelectronics focus on designing and manufacturing electronic devices with mechanical properties close to those of the host biological tissue so that long-term reliability and minimal perturbation are induced in vivo and/or truly wearable systems become possible. We illustrate the potential of this soft technology with examples ranging from prosthetic tactile skins to soft multimodal neural implants.
Empirical Inference
Talk
Chris Bauch
10-07-2017
Sentiment analysis of tweets to detect tipping points in vaccinating behaviour
Vaccine refusal can lead to outbreaks of previously eradicated diseases and is an increasing problem worldwide. Vaccinating decisions exemplify a complex, coupled system where vaccinating behavior and disease dynamics influence one another. Complex systems often exhibit characteristic dynamics near a tipping point to a new dynamical regime. For instance, critical slowing down -- the tendency for a system to start `wobbling'-- can increase close to a tipping point. We used a linear support vector machine to classify the sentiment of geo-located United States and California tweets concern...
Empirical Inference
Talk
Felix Leibfried and Jordi Grau-Moya
13-06-2017
Model-based reinforcement learning for sequential decision-making
Autonomous systems rely on learning from experience to automatically refine their strategy and adapt to their environment, and thereby have huge advantages over traditional hand engineered systems. At PROWLER.io we use reinforcement learning (RL) for sequential decision making under uncertainty to develop intelligent agents capable of acting in dynamic and unknown environments. In this talk we first give a general overview of the goals and the research conducted at PROWLER.io. Then, we will talk about two specific research topics. The first is Information-Theoretic Model Uncertainty which d...
Michel Besserve
Empirical Inference
Talk
Hannes Nickisch, Philips Research, Hamburg
15-08-2016
Learning Patient-Specific Lumped Models for Interactive Coronary Blood Flow Simulations
Coronary artery disease (CAD) is the single leading cause of death worldwide and Cardiac Computed Tomography Angiography (CCTA) is a non-invasive test to rule out CAD using the anatomical characterization of the coronary lesions. Recent studies suggest that coronary lesions’ hemodynamic significance can be assessed by Fractional Flow Reserve (FFR), which is usually measured invasively in the CathLab but can also be simulated from a patient-specific biophysical model based on CCTA data.
We learn a parametric lumped model (LM) enabling fast computational fluid dynamic simulations of blood fl...
Empirical Inference
Talk
Sascha Quantz
01-03-2016
Images of planets orbiting other stars
The detection and characterization of planets orbiting other stars than the Sun, i.e., so-called extrasolar planets, is one of the fastest growing and most vibrant research fields in modern astrophysics. In the last 25 years, more than 5400 extrasolar planets and planet candidates were revealed, but the vast majority of these objects was detected with indirect techniques, where the existence of the planet is inferred from periodic changes in the light coming from the central star. No photons from the planets themselves are detected. In this talk, however, I will focus on the direct detectio...
