Seminar | TransferLab — appliedAI Institute

Seminar

The TransferLab Seminar is a platform where researchers and engineers share and discuss recent advances in AI/ML, striking a balance between accessibility and mathematical depth.

We organize comprehensive talks on ML, RL, DL and statistics, emphasizing both theoretical understanding and practical implementation. These range from introductory overviews to recent developments - and are open to all interested individuals!

Each talk, lasting about 45min followed by around 15min discussion, focuses on a specific topic related to our ongoing series topics. To stay informed, follow the appliedAI Institute on LinkedIn. If you’re interested in presenting your work, contact us. Recordings of past seminars are available on YouTube.

Upcoming Talks

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Previous Talks

Introduction to simulation-based inference - Álvaro Tejero-Cantero (University of Tübingen)

Dec 17, 2020

Scientists and engineers employ stochastic numerical simulators to model empirically observed phenomena. In contrast to purely statistical models, simulators express scientific principles that provide powerful inductive biases, improve generalization to new data or scenarios and allow for fewer, more interpretable and domain-relevant parameters. Despite these advantages, tuning a simulator’s parameters so that its outputs match data is challenging.

Simulation-Based Inference

Álvaro Tejero-Cantero

Hamiltonian Monte Carlo Sampling - Maternus Herold (appliedAI Institute)

Dec 3, 2020

Metropolis, Slice or Gibbs sampling tend to be inefficient when applied in high dimensions due to their local random walk behaviour. Hamiltonian Monte Carlo (HMC) was developed around a geometrical understanding of the target distribution, borrowing concepts from physics to generate transitions and exploring the target distribution. Ultimately, moving more rapidly through the target distribution.

Probabilistic Models

Maternus Herold

Bayesian optimal experiment design (2 of 2) - Markus Semmler (appliedAI Initiative)

Nov 26, 2020

This is the second of a two talks providing an introduction to Bayesian optimal experimental design (BOED) and how it relates to exploration in reinforcement learning and control. The first part dealt with the theoretical foundations for BOED. This talk completes the topic by presenting an application in control, which was created during my master thesis at TUDa.

Bayesian optimal experiment design (1 of 2) - Markus Semmler (appliedAI Initiative)

Nov 19, 2020

These two talks give an introduction to Bayesian optimal experimental design (BOED) and how it relates to exploration in reinforcement learning and control. The first part deals with the theoretical foundations for BOED. Different design types are covered, e.g. space filling designs, alphabetic designs and Bayesian designs. Afterwards it is shown how Bayes’ theorem relates to the mutual information (MI) or equally the expected information gain (EIG).

Solving Schrödinger's equation with neural networks - Omer Rochman

Nov 12, 2020

In this work, we attempt to solve the Time Dependent Schrödinger Equation directly by placing a neural network ansatz on the solution and designing a loss metric based on the constraints imposed by the equation. We show that the method works for simple cases, such as those in 1D or with the free Hamiltonian, but fails in higher dimensions (3D+) or when a complex potential is present.

Simulation and AI

Bayesian ML and Probabilistic Programming 3: Variational Inference - Faried Abu Zaid (appliedAI Institute)

Nov 5, 2020

In this talk, we lay the ground for Markov Chain Monte Carlo (MCMC) methods to inferring the posterior distribution, after looking into the limitations posed by the analytical approach. We demonstrate how MCMC works and use it to solve a Bayesian regression problem and introduce concepts related to causal inference. Before doing so, we have a quick tutorial on using Pyro as a probabilistic programming language.

Probabilistic Models

Faried Abu Zaid

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Our Block Seminars

Methods and issues in Explainable AI

This seminar series explores the landscape of Explainable Artificial Intelligence (XAI) beyond the content in our training on XAI, and through a comprehensive selection of papers in the field. By the end of this series, participants will …

May 17, 2023

Uncertainty quantification for neural networks

In this seminar series, we review seminal and recent papers on uncertainty quantification that didn’t make it into our training on Bayesian ML.

Oct 22, 2022