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Jim Berend

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NeurIPS Conference 2025 Conference Paper

Beyond Scalars: Concept-Based Alignment Analysis in Vision Transformers

  • Johanna Vielhaben
  • Dilyara Bareeva
  • Jim Berend
  • Wojciech Samek
  • Nils Strodthoff

Measuring the alignment between representations lets us understand similarities between the feature spaces of different models, such as Vision Transformers trained under diverse paradigms. However, traditional measures for representational alignment yield only scalar values that obscure how these spaces agree in terms of learned features. To address this, we combine alignment analysis with concept discovery, allowing a fine-grained breakdown of alignment into individual concepts. This approach reveals both universal concepts across models and each representation’s internal concept structure. We introduce a new definition of concepts as non-linear manifolds, hypothesizing they better capture the geometry of the feature space. A sanity check demonstrates the advantage of this manifold-based definition over linear baselines for concept-based alignment. Finally, our alignment analysis of four different ViTs shows that increased supervision tends to reduce semantic organization in learned representations.

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TMLR Journal 2025 Journal Article

Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation

  • Leander Weber
  • Jim Berend
  • Moritz Weckbecker
  • Alexander Binder
  • Thomas Wiegand
  • Wojciech Samek
  • Sebastian Lapuschkin

Gradient-based optimization has been a cornerstone of machine learning that enabled the vast ad- vances of Artificial Intelligence (AI) development over the past decades. However, this type of optimization requires differentiation, and with recent evidence of the benefits of non-differentiable (e.g. neuromorphic) architectures over classical models w.r.t. efficiency, such constraints can be- come limiting in the future. We present Layer-wise Feedback Propagation (LFP), a novel training principle for neural network-like predictors that utilizes methods from the domain of explainability to decompose a reward to individual neurons based on their respective contributions. Leveraging these neuron-wise rewards, our method then implements a greedy approach reinforcing helpful parts of the network and weakening harmful ones. While having comparable computational complexity to gradient descent, LFP does not require gradient computation and generates sparse and thereby memory- and energy-efficient parameter updates and models. We establish the convergence of LFP theoretically and empirically, demonstrating its effectiveness on various models and datasets. Via two applications — neural network pruning and the approximation-free training of Spiking Neural Networks (SNNs) — we demonstrate that LFP combines increased efficiency in terms of computation and representation with flexibility w.r.t. choice of model architecture and objective function.

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