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

Control of non-linear gas rectification plants - Nicolas Blum

Feb 21, 2019

appliedAI team only!

Dealing with imbalanced classes - Wolfgang Reuter

Feb 21, 2019

In this talk Wolfgang shares some of his personal experience and methods while training classifier CNNs on image datasets with highly imbalanced classes.

Data Efficiency

Wolfgang Reuter

Intro to Reinforcement Learning - Immanuel schwall

Feb 14, 2019

Based on the course at Udacity: Deep Reinforcement Learning Nanodegree

Immanuel schwall

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning - Fabian Hainzl

Feb 14, 2019

Paper abstract: Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good performance. Model-based algorithms, in principle, can provide for much more efficient learning, but have proven difficult to extend to expressive, high-capacity models such as deep neural networks.

Data Efficiency

Reinforcement Learning

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding - Sebastian Wagner

Feb 7, 2019

Generative Models

Sebastian Wagner

A short introduction to model-based Reinforcement Learning - Miguel de Benito Delgado

Jan 24, 2019

Model-based RL learns a deterministic or probabilistic forward model of the dynamics of an environment for action planning or direct policy optimisation. This knowledge enables agents to learn with up to orders of magnitude fewer samples than their model-free counterparts. The cost however is lower maximal performance, due to model misspecification.

Data Efficiency

Reinforcement Learning

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