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A survey of general results on the singularities of inverses to meromorphic functions is given, with applications to holomorphic dynamics. This is a lecture delivered at the workshop "The role of complex analysis in complex dynamics" in…

Complex Variables · Mathematics 2021-10-13 Alexandre Eremenko

Developing robust representations of chemical structures that enable models to learn topological inductive biases is challenging. In this manuscript, we present a representation of atomistic systems. We begin by proving that our…

Machine Learning · Computer Science 2024-09-27 Rahul Khorana , Marcus Noack , Jin Qian

By complexifying a Hamiltonian system one obtains dynamics on a holomorphic symplectic manifold. To invert this construction we present a theory of real forms which not only recovers the original system but also yields different real…

Symplectic Geometry · Mathematics 2025-01-03 Philip Arathoon , Marine Fontaine

We present an explicit and computationally actionable blueprint for constructing vector-valued Siegel modular forms associated to real multiplication (RM) abelian surfaces, leveraging the theta correspondence for the unitary dual pair…

Number Theory · Mathematics 2025-02-12 Robin Jackson

Transformers trained on huge text corpora exhibit a remarkable set of capabilities, e.g., performing basic arithmetic. Given the inherent compositional nature of language, one can expect the model to learn to compose these capabilities,…

Machine Learning · Computer Science 2024-02-07 Rahul Ramesh , Ekdeep Singh Lubana , Mikail Khona , Robert P. Dick , Hidenori Tanaka

Representation learning on text-attributed graphs (TAGs) integrates structural connectivity with rich textual semantics, enabling applications in diverse domains. Current methods largely rely on contrastive learning to maximize cross-modal…

Graphics · Computer Science 2025-10-15 Heng Zhang , Tianyi Zhang , Yuling Shi , Xiaodong Gu , Yaomin Shen , Zijian Zhang , Yilei Yuan , Hao Zhang , Jin Huang

Creating representations of shapes that are invari-ant to isometric or almost-isometric transforma-tions has long been an area of interest in shape anal-ysis, since enforcing invariance allows the learningof more effective and robust shape…

Computer Vision and Pattern Recognition · Computer Science 2021-07-09 Jeffrey Gu , Serena Yeung

These are lecture notes for a course in Winter 2022/23, updated and completed in October 2025. The goal of the lectures is to present some recent developments around six-functor formalisms, in particular: the abstract theory of 6-functor…

Algebraic Geometry · Mathematics 2026-01-23 Peter Scholze

This communication is an introduction to the Langlands Program and to ($G$-) shtukas (over algebraic curves) over function fields. Modular curves and Drinfeld (elliptic) modules and shtukas are used in coding theory. From this point of view…

Number Theory · Mathematics 2020-04-27 Nikolaj Glazunov

We give an introduction to the structure theory of extended affine Lie algebras, which provide a common framework for finite-dimensional semisimple, affine and toroidal Lie algebras. The notes are based on a lecture series given during the…

Rings and Algebras · Mathematics 2013-12-17 Erhard Neher

Most recent work on interpretability of complex machine learning models has focused on estimating $\textit{a posteriori}$ explanations for previously trained models around specific predictions. $\textit{Self-explaining}$ models where…

Machine Learning · Computer Science 2018-12-05 David Alvarez-Melis , Tommi S. Jaakkola

This is an expository article based on my lectures on eigenfunctions of the Laplacian for the 2013 IAS/Park City Mathematics Institute (PCMI) summer school in geometric analysis. Many of the results are based on joint work with H.…

Analysis of PDEs · Mathematics 2016-12-13 Steve Zelditch

Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval. Though recent deep learning methods can partially solve the problem, they often fail to handle the…

Information Retrieval · Computer Science 2023-05-30 Kaize Ding , Albert Jiongqian Liang , Bryan Perrozi , Ting Chen , Ruoxi Wang , Lichan Hong , Ed H. Chi , Huan Liu , Derek Zhiyuan Cheng

These notes are an account of a series of lectures I gave at the LMS-CMI Research School `Homotopy Theory and Arithmetic Geometry: Motivic and Diophantine Aspects', in July 2018, at the Imperial College London. The goal of these notes is to…

Algebraic Geometry · Mathematics 2020-08-11 Denis-Charles Cisinski

The advent of representation learning methods enabled large performance gains on various language tasks, alleviating the need for manual feature engineering. While engineered representations are usually based on some linguistic…

Computation and Language · Computer Science 2018-10-17 Ahmad Taie , Raphael Rubino , Josef van Genabith

Deploying machine learning models in safety-related do-mains (e.g. autonomous driving, medical diagnosis) demands for approaches that are explainable, robust against adversarial attacks and aware of the model uncertainty. Recent deep…

Computer Vision and Pattern Recognition · Computer Science 2020-12-14 Jan Kronenberger , Anselm Haselhoff

These are notes based on a series of lectures given at the KITP workshop "Quantum Criticality and the AdS/CFT Correspondence" in July, 2009. The goal of the lectures was to introduce condensed matter physicists to the AdS/CFT…

High Energy Physics - Theory · Physics 2014-11-20 John McGreevy

These notes were prepared for the introductory lectures on Higher Spin Holography presented at the Twelfth Modave Summer School in Mathematical Physics 2016, aimed at Ph.D. students and researchers new to this topic. Contents: 1. Higher…

High Energy Physics - Theory · Physics 2017-03-01 Charlotte Sleight

A study of assisted problem solving formalized via decompositions of deterministic finite automata is initiated. The landscape of new types of decompositions of finite automata this study uncovered is presented. Languages with various…

Computational Complexity · Computer Science 2007-07-04 Peter Gaži , Branislav Rovan

Despite tremendous progress over the past decade, deep learning methods generally fall short of human-level systematic generalization. It has been argued that explicitly capturing the underlying structure of data should allow connectionist…

Machine Learning · Computer Science 2023-04-26 Andrea Dittadi
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