# Tag: networks

## Algorithms for Stochastic Analysis Using SageMath

summary: This talk explores algorithms for computations in stochastic analysis and their increasing relevance in modern mathematics. We present algorithms to explicitly solve Stochastic Differential Equations via Itô's Lemma, and derive solutions of certain partial differential equations using Feynman–Kac formula. Hence, emphasizing the need for developing SageMath packages ...

## Travo: a classroom open source Python toolkit

summary: Teaching computer science or computational courses inevitably implies collaboration on code. Software forges can provide helpful infrastructure to support and improve collaboration in teaching workflows. This talk will present Travo, an open source Python toolkit turning your favorite forge into a flexible management solution for computer assignments. Travo is ...

## The Canadian Traveller Problem on outerplanar graphs

summary: We focus on the PSPACE-complete k-Canadian Traveller Problem, where a weighted graph with a source s and a target t are given. This problem also has a hidden input of cardinality at most k representing blocked edges. The objective is to travel from s to t with the minimum ...

## TBA

summary: TBA

## Canadian Traveller Problems in Temporal Graphs.

summary: We focus on the Canadian Traveller Problem, where a traveller aims to travel on a network from s to t with the minimum cost, considering that a maximum of k edges can be blocked. These edges remain hidden from the traveller until they visit one of their endpoints. We ...

## Current trends in Optimal Stopping Problems and Machine Learning

summary: Stochastic optimal stopping problems have a wide range of applications, from finance and economics to neuroscience,
robotics, and energy management. Many real-world applications involve complex models that have driven the development of
sophisticated numerical methods.

Recently, computational methods based on machine learning methods have been developed for solving such ...

## Complexity of neural network training and complexity proofs bypassing frontier issues

summary: We study the complexity of the neural network training decision problem in two different contexts. First, in the general context, this problem has been shown to be in extensions of the class ∃R. We have been able to show that whenever the activation functions are Lipschitz functions and the ...

## Complexité en états : Renverser un langage réduit la complexité de l'opération racine.

summary: Les automates (DFA) sont des machines à états qui acceptent ou rejettent des mots. L'ensemble des mots reconnus par un automate est son langage. Les langages rationnels coïncident avec les langages reconnaissable par des automates. Ici nous allons nous intéresser à une mesure, à savoir la complexité en ...

## Forecasting multivariate time series with attention mechanism and unsupervised learning

summary: In the realm of newborn healthcare, identifying neurological pathologies has traditionally relied on the expertise of medical professionals, who perform visual assessments. However, due to the limited number of such experts available, there is an urgent need to develop a pre-diagnostic tool capable of early detection of abnormal neurological ...

## Quantifiying the robustness of dynamical systems: relating time and space to length and precision

summary: Reasoning about dynamical systems evolving over the reals is well-known to lead to undecidability. In particular, it is known there cannot be decision procedures for first-order theories over the reals, or decision procedures for state reachability. However, various results in the literature have shown that decision procedures exist when ...

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