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ELLIS UniReps Speaker Series

We’re excited to launch a Speaker Series in collaboration with European Laboratory for Learning and Intelligent Systems (ELLIS) community, focusing on key topics relevant to our community.

When, How, and Why Do Neural Models Learn Similar Representations? The ELLIS UniReps Speaker Series explores the phenomenon of representational alignment, where different neural models—both biological and artificial—develop similar internal representations when exposed to comparable stimuli. This raises key theoretical and practical questions:

  • When do similar representations emerge across models?
  • Why does this alignment occur, and what underlying principles drive it?
  • How can we leverage this alignment to explore applications such as model merging, model re-use, and fine-tuning?

Each monthly session features two talks:

  • 🔵 Keynote talk – A broad overview by a senior researcher, providing context on a key topic.
  • 🔴 Flash talk – A focused presentation by an early-career researcher (such as a PhD student or postdoc), highlighting recent findings or ongoing work.

You can nominate yourself or another researcher as a speaker by filling out our nomination form .

The series provides a platform for early-career researchers to share their work and fosters interdisciplinary discussions across deep learning, neuroscience, cognitive science, and mathematics.

Below you can find the calendar for next schduled appointments:

Calendar

May Appointment

  • 🗓️ When: 29th May 2025 – 16:30 CET
  • 📍 Where: Zoom link
  • 🎙️ Keynote: Andrew Lampinen (Google Deepmind)
    • Title: Representation Biases: when aligned representations do not imply aligned computations
    • Abstract: We often study a system’s representations to learn about its computations, or intervene on its representations to try to fix it. However, the relationship between representation and computation is not always straightforward. In this talk, I will discuss a recent paper (https://openreview.net/forum?id=aY2nsgE97a) in which we study this relationship in controlled settings. We find that feature representations are substantially biased towards certain types of features (linear over nonlinear, prevalent over less prevalent), even when the features play an equivalent computational role in the model’s outputs. These phenomena hold across a wide range of models and tasks. I will discuss implications of these feature biases for downstream analyses like regression and RSA, and their relation to our recent finding that simplifying models for analysis may not generalize well out of distribution (https://openreview.net/forum?id=YJWlUMW6YP). These results raise important questions over how to interpret and use representational analysis tools.
  • 🎙️ Flash Talk: Jack Lindsey (Anthropic)
    • Title: On the Biology of a Large Language Model
    • Abstract: Large language models display impressive capabilities. However, for the most part, the mechanisms by which they do so are unknown. The black-box nature of models is increasingly unsatisfactory as they advance in intelligence and are deployed in a growing number of applications. Our goal is to reverse engineer how these models work on the inside, so we may better understand them and assess their fitness for purpose.ntroduce a new set of tools for identifying features and mapping connections between them – analogous to neuroscientists producing a “wiring diagram” of the brain. We rely heavily on a tool we call attribution graphs, which allow us to partially trace the chain of intermediate steps that a model uses to transform a specific input prompt into an output response. Attribution graphs generate hypotheses about the mechanisms used by the model, which we test and refine through follow-up perturbation experiments. We investigate the internal mechanisms used by Claude 3.5 Haiku — Anthropic’s lightweight production model — in a variety of contexts, using our circuit tracing methodology.