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

Bayesian ML and Probabilistic Programming: Introduction - Surya Kocherlakota

Jul 9, 2020

In this seminar, we go back to the very basics with Surya, who will (re)visit the fundamentals of the Bayesian approach to ML, including Maximum Likelihood and Maximum A Posteriori Estimation. He will introduce approximate Bayesian inference using PyMC3 (a Probabilistic Programming Language), which will serve as a springboard for understanding future material in this stream, such as Variational Methods, Graphical models, etc.

Probabilistic Models

Surya Kocherlakota

A soft introduction to causal inference - Lukas Dreier

Jun 19, 2020

‘Correlation does not imply causation’ is a famous saying in statistics. The goal of many (scientific) studies is to draw conclusions on whether a so-called “treatment” has some effect or not, e.g. assigning a drug, or showing homepage A or homepage B to a visitor. As mentioned, it is in general not sufficient to observe statistical association of phenomena to infer causal relationships, but it is possible to make assumptions allowing for it.

Causal Inference and Discovery

Problems and solutions in offline learning - Robert Müller (appliedAI Institute)

Apr 30, 2020

Current reinforcement learning algorithms require tons of data to solve new tasks. This “hunger for data” is especially onerous in high stake scenarios like healthcare or education, where on-policy data collection is infeasible. This motivates the Batch RL setting where the learner is only given a batch of transition tuples but allowed no further interactions with the environment.

Reinforcement Learning

Solving PDEs with neural networks - Omer Rochman

Apr 23, 2020

We’ll be talking about PDEs and how to solve them numerically using NNs. If you have no idea what those are you should probably read a little on it because we won’t be introducing them too much. Check the 3Blue1Brown video for some cool visuals. To say that PDEs are important in Science and Engineering would be a ridiculous understatement.

Simulation and AI

Isolation Forests: The good, the bad and the ugly - Faried Abu Zaid (appliedAI Institute)

Apr 2, 2020

Anomaly detection is one of the main methods behind numerous real life machine learning use cases such as predictive maintenance, network intrusion detection, system health monitoring, fraud detection and novelty detection. Because of the high relevance and sensitivity of many application areas, robustness and reliability are main concerns when designing anomaly detection systems.

Anomaly Detection

Faried Abu Zaid

The calibrated classifier - Miguel de Benito Delgado

Mar 19, 2020

Informed decision-making based on classifiers requires that the confidence in their predictions reflect the actual error rates. When this happens, one speaks of a calibrated model. Recent work showed that expressive neural networks are able to overfit the cross-entropy loss without losing accuracy, thus producing overconfident (i.e. miscalibrated) models. We analyse several definitions of calibration and the relationships between them, look into related empirical measures and their usefulness, and explore several algorithms to improve calibration.

Classifier Calibration

Miguel de Benito Delgado

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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