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

Nothing here yet!

Previous Talks

Postponed

Unravelling complexity in neuronal time-series data with BunDLe-Net - Akshey Kumar (TU Vienna)

Jun 27, 2024

Akshey Kumar, postdoctoral member of the Neuroinformatics research group at TU Vienna, will talk about BunDLe-Net, a manifold-learning algorithm that effectively preserves relevant information while abstracting away details that are irrelevant to the dynamics of a specific target variable.

Probabilistic Models

Done

Inducing Point Operator Transformer: A Flexible and Scalable Architecture for Solving PDEs - Seungjun Lee (Alsemy, South Korea)

Jun 20, 2024

Seungjun Lee will talk about an attention-based neural operator architecture called an Inducing Point Operator Transformer (IPOT), which addresses the challenges of flexibility in handling irregular and arbitrary input and output formats and scalability to large discretizations when solving partial differential equations (PDEs).

Simulation and AI

Done

Dense Rewards and Continual RL for Task-Oriented Dialogue Policies - Christian Geishauser (appliedAI Initiative)

Jun 6, 2024

Christian will present a proposal for dense rewards in task-oriented dialogue systems to enhance sample efficiency and discuss continual reinforcement learning of dialogue policies. Key topics include an architecture for continual learning, an extended learning environment, lifetime return optimization, and meta-reinforcement learning for hyperparameter adaptation.

Reinforcement Learning

Done

Scientific Inference With Interpretable Machine Learning - Timo Freiesleben (University of Tübingen)

May 23, 2024

Timo will introduce a framework for designing interpretable machine learning methods for science, termed “property descriptors”.

Explainable AI

Done

LAVA: Data Valuation Without Pre-Specified Learning Algorithms - Feiyang Kang (Virginia Tech)

May 2, 2024

Today’s talk is about LAVA, an Optimal-Transport-based approach to data valuation that dispenses with training of a model to compute values.

Data Valuation

Done

RAGAR, Your Falsehood RADAR: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models - Mohammed Abdul Khaliq (University of Stuttgart)

Apr 18, 2024

Mohammed Abdul Khaliq, MSc. Computational Linguistics Programm at the Institute for Natural Language Processing of the University of Stuttgart, will give a talk on the topic of his Master’s thesis: “RAGAR, Your Falsehood RADAR: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models”.

Large Language Models

Mohammed Abdul Khaliq

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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