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

An information-theoretic perspective on model interpretation - Kristof Schröder (appliedAI Institute)

Oct 12, 2023

In the ninth seminar of our XAI series, Kristof Schröder, Senior Research Engineer at appliedAI, will discuss how maximizing mutual information between selected features and the response variable can aid in model interpretation, by offering a unique, information-theoretic perspective on AI models. Abstract: Providing explainability in a model-agnostic way is a challenging task.

Kristof Schröder

Effects of XAI on perception, trust and acceptance - Maternus Herold (appliedAI Institute)

Oct 5, 2023

This talk delves into the influence of Explainable Artificial Intelligence (XAI) on human cognition, trust, and acceptance of AI-driven systems. Through a review of empirical studies, this presentation illuminates how the provision of intelligible explanations shapes individuals’ perception of AI-generated outputs. By synthesizing findings from diverse contexts, we uncover critical insights into the mechanisms underlying the cognitive processing of explanations, shedding light on the factors that modulate trust and acceptance levels.

Maternus Herold

Latent space prototype interpretability: Strengths and shortcomings - Giorgia Pitteri

Sep 28, 2023

Prototype-based approaches aim at training intrinsically interpretable models that nevertheless are as powerful as typical black-box neural networks. We introduce the main ideas behind this concept by explaining the original Prototypical Part Network (ProtoPNet) and the most recent Neural Prototype Tree (ProtoTree) model which combines prototypical learning with decision trees. We introduce some limitations of these approaches by underling the need to enhancing visual prototypes with textual quantitative information to understand better what a prototype represents.

Giorgia Pitteri

Influence Diagnostics Under Self-Concordance - Jillian Fisher

Sep 21, 2023

In our sixth seminar, we have the pleasure of receiving Jillian Fisher from the statistics department of the University of Washington, who will be presenting her recent work accepted at AISTATS2023, “Influence Diagnostics under Self-concordance”. Abstract: Influence diagnostics such as influence functions and approximate maximum influence perturbations are popular in machine learning and in AI domain applications.

Manifold Restricted Interventional Shapley Values - Muhammad Faaiz Taufiq

Sep 14, 2023

For our fifth installment in this series we are happy to host Muhammad Faaiz Taufiq, from Oxford University. Faaiz will introduce his recent work presented at AISTATS2023: ManifoldShap, a novel method for computing Shapley values that effectively circumvents the limitations of off-manifold and on-manifold methods. Abstract: Shapley values are model-agnostic methods for explaining model predictions.

Muhammad Faaiz Taufiq

Influence functions and Data Pruning: from theory to non-convergence - Fabio Peruzzo (appliedAI Initiative)

Jun 15, 2023

Today’s session brings Influence Functions under the spotlight - the theory, non-convergence issues, and uses for data pruning. Fabio will uncover the fragile nature of influence functions in deep learning, helping us understand what neural networks memorize, and exploring the possibility of beating power law scaling of model performance with dataset size.

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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