Search results for “topoi”

The problem of probabilistic topic modeling is as follows. Given a collection of text documents, find the conditional distribution over topics for each document and the conditional distribution over words (or terms) for each topic. Log-likelihood maximization is used to solve this problem. The problem generally has an infinite set of solutions and is ill-posed according to Hadamard. In the framework of Additive Regularization of Topic Models (ARTM), a weighted sum of regularization criteria is added to the main log-likelihood criterion. The numerical method for solving this optimization problem is a kind of an iterative EM-algorithm written in a general form for an arbitrary smooth regularizer as well as for a linear combination of smooth regularizers. This paper studies the problem of convergence of the EM iterative process. Sufficient conditions are obtained for the convergence to a stationary point of the regularized log-likelihood. The constraints imposed on the regularizer are not too restrictive. We give their interpretations from the point of view of the practical implementation of the algorithm. A modification of the algorithm is proposed that improves the convergence without additional time and memory costs. Experiments on a news text collection have shown that our modification both accelerates the convergence and improves the value of the criterion to be optimized.

Modern natural language processing models such as transformers operate multimodal data. In the present paper, multimodal data is explored using multimodal topic modeling on transactional data of bank corporate clients. A definition of the importance of modality for the model is proposed on the basis of which improvements are considered for two modeling scenarios: preserving the maximum amount of information by balancing modalities and automatic selection of modality weights to optimize auxiliary criteria based on topic representations of documents. A model is proposed for adding numerical data to topic models in the form of modalities: each topic is assigned a normal distribution with learning parameters. Significant improvements are demonstrated in comparison with standard topic models on the problem of modeling bank corporate clients. Based on the topic representations of the bank’s customers, a 90-day delay on the loan is predicted.

Social studies of the Internet have adopted large-scale text mining for unsupervised discovery of topics related to specific subjects. A recently developed approach to topic modeling, additive regularization of topic models (ARTM), provides fast inference and more control over the topics with a wide variety of possible regularizers than developing LDA extensions. We apply ARTM to mining ethnic-related content from Russian-language blogosphere, introduce a new combined regularizer, and compare models derived from ARTM with LDA. We show with human evaluations that ARTM is better for mining topics on specific subjects, finding more relevant topics of higher or comparable quality.

We propose a generalized probabilistic topic model of text corpora which can incorporate heuristics of Bayesian regularization, sampling, frequent parameters update, and robustness in any combinations. Wellknown models PLSA, LDA, CVB0, SWB, and many others can be considered as special cases of the proposed broad family of models. We propose the robust PLSA model and show that it is more sparse and performs better that regularized models like LDA.

Где симплициальные гомологии встречаются в жизни — топологический анализ данных. Объединение шаров вокруг облака точек, персистентные модули и их разложение в сумму интервальных модулей над полем (структурная теорема и алгоритм), баркод; комплексы Виеториса–Рипса и Чеха; теорема об устойчивости баркода, расстояния Хаусдорфа, Громова–Хаусдорфа и Васерштейна; открытые покрытия и их нерв, покрытия Лере и теорема о нерве, категория покрытий, паракомпактность, размерность по Лебегу, гомологии и когомологии Чеха.

Сингулярные гомологии — стандартные симплексы и вложения гиперграней, сингулярные симплексы и цепи, дифференциал; нулевые гомологии и компоненты линейной связности, гомологии точки, приведённые гомологии; цепные отображения и отображения гомологий, функториальность, цепное отображение, индуцированное непрерывным отображением; цепные гомотопии и призма; теорема о гомотопической инвариантности с полным доказательством через призменный оператор; подкомплексы, факторкомплексы и гомологии пары.

Задачи курса «Топология-2» (Ф. Е. Вылегжанин, НМУ, осень 2026), листок 1 от 9 сентября 2026 г. — симплициальные гомологии и цепные комплексы. Нулевые гомологии и компоненты связности; гомологии несвязного объединения и букета; триангуляции окружности, сферы, ленты Мёбиуса; джойн и произведение симплициальных комплексов; эйлерова характеристика и неравенства Морса; число вершин триангуляции поверхности; операции над цепными комплексами; точные последовательности; нормальная форма Смита и разложение комплекса.

Задачи курса «Топология-2» (Ф. Е. Вылегжанин, НМУ, осень 2026), листок 2 от 16 сентября 2026 г. — сингулярные гомологии и цепные гомотопии. Цепное отображение из симплициальных цепей в сингулярные; ретракция и прямое слагаемое; гомоморфизм Гуревича из фундаментальной группы в первые гомологии; конечные CW-комплексы и классы гомологий; гомологии объединения цепочки пространств; гомологии симплекса и его остова; симплициальные отображения; цепная гомотопическая эквивалентность комплекса и его гомологий; цепная гомотопность как отношение эквивалентности; 5-лемма.

Modern AI systems often comprise multiple learnable components that can be naturally organized as graphs. A central challenge is the end-to-end training of such systems without restrictive architectural or training assumptions. Such tasks fit the theory and approaches of the collaborative Multi-Agent Reinforcement Learning (MARL) field. We introduce Reinforcement Networks, a general framework for MARL that organizes agents as vertices in a directed acyclic graph (DAG). This structure extends hierarchical RL to arbitrary DAGs, enabling flexible credit assignment and scalable coordination while avoiding strict topologies, fully centralized training, and other limitations of current approaches. We formalize training and inference methods for the Reinforcement Networks framework and connect it to the LevelEnv concept to support reproducible construction, training, and evaluation. We demonstrate the effectiveness of our approach on several collaborative MARL setups by developing several Reinforcement Networks models that achieve improved performance over standard MARL baselines. Beyond empirical gains, Reinforcement Networks unify hierarchical, modular, and graph-structured views of MARL, opening a principled path toward designing and training complex multi-agent systems. We conclude with theoretical and practical directions - richer graph morphologies, compositional curricula, and graph-aware exploration. That positions Reinforcement Networks as a foundation for a new line of research in scalable, structured MARL.

The Millennium Meeting, Paris, 24 May 2000, at which the seven problems were announced.