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Autumn Semester 2026

Date / Time Speaker Title Location
* 8 October 2026
13:45-15:00 		Yatin Dandi
EPFL
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DACO Seminar

Title Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning
Speaker, Affiliation Yatin Dandi, EPFL
Date, Time 8 October 2026, 13:45-15:00
Location HG D 3.3
Abstract Understanding how deep neural networks learn useful internal representations from data remains a central open problem in the theory of deep learning. We introduce Neural Low-Degree Filtering (Neural LoFi), a stylized limit of gradient-based training in which hierarchical feature learning becomes an explicit iterative spectral procedure. In this limit, the dynamics at each layer decouple: given the current representation, the next layer selects directions with maximal accessible low-degree correlation to the label. This yields a tractable surrogate mechanism for deep learning, together with a natural kernel-space interpretation. Neural LoFi provides a mathematically explicit framework for studying multi-layer feature learning beyond the lazy regime. It predicts how representations are selected layer by layer, explains how emergence of concepts arises with given sample complexity, and gives a concrete mechanism by which depth progressively constructs new features from old ones through low-degree compositionality. We complement the theory with mechanistic experiments on fully connected and convolutional architectures, showing that Neural LoFi improves over lazy random-feature baselines, recovers meaningful structured filters, and predicts representations aligned with early gradient-descent feature discovery with real datasets. The talk is based on joint work with Matteo Vilucchio, Luca Arnaboldi, Hugo Tabanelli, and Florent Krzakala (https://arxiv.org/abs/2605.13612).
Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learningread_more
HG D 3.3
22 October 2026
13:45-14:45 		Enli Chen
TU Delft
Details

DACO Seminar

Title Strongly convergent random matrix models for q-Gaussian variables
Speaker, Affiliation Enli Chen, TU Delft
Date, Time 22 October 2026, 13:45-14:45
Location HG G 19.1
Abstract q-Gaussian variables form a family of noncommutative random variables interpolating between free and classical Gaussian behavior. A natural question is whether they admit finite-dimensional random matrix models that converge not only in moments, but also strongly, meaning that the operator norms of all noncommutative polynomials converge. I will explain our construction of strongly convergent random matrix models for finite families of q-Gaussian variables. The construction proceeds in two stages. First, normalized sums of graph-product (Γ-independent) semicircular variables converge strongly to finite q-Gaussian families. Second, the corresponding graph-product semicircular systems are realized as strong limits of finite-dimensional tensor-GUE random matrices. Both steps admit matrix-coefficient versions, and the resulting random-matrix convergence is uniform for growing coefficient dimensions that may even exceed the dimension of the random matrices themselves. Our current arXiv version treats the range |q| < √2 − 1, and we have recently extended the result to the full range |q|<1. I will conclude with a brief discussion of recent connections between our construction and q-Gaussian limits arising from Kikuchi matrices.
Strongly convergent random matrix models for q-Gaussian variablesread_more
HG G 19.1
* 23 October 2026
14:15-15:15 		Prof. Dr. Jorge Garza-Vargas
MIT, USA
Details

DACO Seminar

Title TBD
Speaker, Affiliation Prof. Dr. Jorge Garza-Vargas, MIT, USA
Date, Time 23 October 2026, 14:15-15:15
Location HG D 3.3
TBD
HG D 3.3
5 November 2026
13:15-15:15 		Dr. Tom Szwagier
IRIT, Toulouse, France
Details

DACO Seminar

Title TBD
Speaker, Affiliation Dr. Tom Szwagier, IRIT, Toulouse, France
Date, Time 5 November 2026, 13:15-15:15
Location HG G 19.1
TBD
HG G 19.1

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