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Chain-of-thought (CoT) significantly enhances the performance of large language models (LLMs) across a wide range of tasks, and prior research shows that CoT can theoretically increase expressiveness. However, there is limited mechanistic…

Computation and Language · Computer Science 2025-06-04 Yifan Zhang , Wenyu Du , Dongming Jin , Jie Fu , Zhi Jin

We investigate the modeling and the numerical solution of machine learning problems with prediction functions which are linear combinations of elements of a possibly infinite-dimensional dictionary. We propose a novel flexible composite…

Statistics Theory · Mathematics 2015-12-03 Patrick L. Combettes , Saverio Salzo , Silvia Villa

We propose a generic categorical framework for learning unknown formal languages of various types (e.g. finite or infinite words, weighted and nominal languages). Our approach is parametric in a monad T that represents the given type of…

Formal Languages and Automata Theory · Computer Science 2020-08-31 Henning Urbat , Lutz Schröder

This work introduces a non-intrusive model reduction approach for learning reduced models from partially observed state trajectories of high-dimensional dynamical systems. The proposed approach compensates for the loss of information due to…

Machine Learning · Computer Science 2021-03-29 Wayne Isaac Tan Uy , Benjamin Peherstorfer

Constructing complex computation from simpler building blocks is a defining problem of computer science. In algebraic automata theory, we represent computing devices as semigroups. Accordingly, we use mathematical tools like products and…

Group Theory · Mathematics 2025-05-06 Attila Egri-Nagy , Chrystopher L. Nehaniv

Few-Shot Learning is the challenge of training a model with only a small amount of data. Many solutions to this problem use meta-learning algorithms, i.e. algorithms that learn to learn. By sampling few-shot tasks from a larger dataset, we…

Computer Vision and Pattern Recognition · Computer Science 2019-10-01 Etienne Bennequin

Reactive synthesis is the task of automatically deriving a correct implementation from a specification. It is a promising technique for the development of verified programs and hardware. Despite recent advances in terms of algorithms and…

Logic in Computer Science · Computer Science 2021-12-17 Bernd Finkbeiner , Gideon Geier , Noemi Passing

On-line and batch learning of a perceptron in a discrete weight space, where each weight can take $2 L+1$ different values, are examined analytically and numerically. The learning algorithm is based on the training of the continuous…

Statistical Mechanics · Physics 2009-11-07 Michal Rosen-Zvi , Ido Kanter

We propose a new learning-based approach to solve ill-posed inverse problems in imaging. We address the case where ground truth training samples are rare and the problem is severely ill-posed - both because of the underlying physics and…

Computer Vision and Pattern Recognition · Computer Science 2018-12-07 Sidharth Gupta , Konik Kothari , Maarten V. de Hoop , Ivan Dokmanić

The integration of machine learning and robotics into thin film deposition is transforming material discovery and optimization. However, challenges remain in achieving a fully autonomous cycle of deposition, characterization, and…

Materials Science · Physics 2026-02-24 Yuanlong Zheng , Connor Blake , Layla Mravac , Fengxue Zhang , Yuxin Chen , Shuolong Yang

We revisit the problem of reducing incompletely specified Mealy machines with reactive synthesis in mind. We propose two techniques: the former is inspired by the tool MeMin and solves the minimization problem, the latter is a novel…

Formal Languages and Automata Theory · Computer Science 2022-06-22 Florian Renkin , Philipp Schlehuber-Caissier , Alexandre Duret-Lutz , Adrien Pommellet

A novel approach to reduced-order modeling of high-dimensional time varying systems is proposed. It leverages the formalism of the Dynamic Mode Decomposition technique together with the concept of balanced realization. It is assumed that…

Systems and Control · Electrical Eng. & Systems 2021-06-01 Andrea Iannelli , Urban Fasel , Roy S. Smith

A hallmark of human intelligence is the ability to construct self-contained chunks of knowledge and adequately reuse them in novel combinations for solving different yet structurally related problems. Learning such compositional structures…

Machine Learning · Computer Science 2021-03-18 Jorge A. Mendez , Eric Eaton

Federated learning is a machine learning protocol that enables a large population of agents to collaborate over multiple rounds to produce a single consensus model. There are several federated learning applications where agents may choose…

Machine Learning · Computer Science 2023-11-30 Minbiao Han , Kumar Kshitij Patel , Han Shao , Lingxiao Wang

Machine unlearning is an emerging technology that removes a subset of the training data from a trained model without significantly affecting the model performance on the remaining data. This topic is becoming increasingly important in…

Machine Learning · Computer Science 2026-05-12 Laiqiao Qin , Tianqing Zhu , Linlin Wang , Wanlei Zhou

We present a continuous formulation of machine learning, as a problem in the calculus of variations and differential-integral equations, in the spirit of classical numerical analysis. We demonstrate that conventional machine learning models…

Numerical Analysis · Mathematics 2020-10-02 Weinan E , Chao Ma , Lei Wu

Model reduction of high-dimensional dynamical systems alleviates computational burdens faced in various tasks from design optimization to model predictive control. One popular model reduction approach is based on projecting the governing…

Dynamical Systems · Mathematics 2018-08-24 Francisco J. Gonzalez , Maciej Balajewicz

Automata learning is a technique to automatically infer behavioral models of black-box systems. Today's learning algorithms enable the deduction of models that describe complex system properties, e.g., timed or stochastic behavior. Despite…

Formal Languages and Automata Theory · Computer Science 2023-05-25 Andrea Pferscher , Bernhard K. Aichernig

Knowledge distillation is an effective method for training small and efficient deep learning models. However, the efficacy of a single method can degenerate when transferring to other tasks, modalities, or even other architectures. To…

Computer Vision and Pattern Recognition · Computer Science 2024-03-12 Roy Miles , Ismail Elezi , Jiankang Deng

Automatic differentiation plays a prominent role in scientific computing and in modern machine learning, often in the context of powerful programming systems. The relation of the various embodiments of automatic differentiation to the…

Programming Languages · Computer Science 2020-02-04 Martin Abadi , Gordon D. Plotkin