Zobrazeno 1 - 10
of 464 878
pro vyhledávání: '"General Framework"'
This work investigates the effectiveness of schedule-free methods, developed by A. Defazio et al. (NeurIPS 2024), in nonconvex optimization settings, inspired by their remarkable empirical success in training neural networks. Specifically, we show th
Externí odkaz:
http://arxiv.org/abs/2411.07061
Imaging genetics is a growing field that employs structural or functional neuroimaging techniques to study individuals with genetic risk variants potentially linked to specific illnesses. This area presents considerable challenges to statisticians du
Externí odkaz:
http://arxiv.org/abs/2412.19735
Autor:
Chau, Huy N., Rasonyi, Miklos
In this paper, a new approach for solving the problems of pricing and hedging derivatives is introduced in a general frictionless market setting. The method is applicable even in cases where an equivalent local martingale measure fails to exist. Our
Externí odkaz:
http://arxiv.org/abs/2411.19206
This paper introduces the concept of abstracted model reduction: a framework to improve the tractability of structure-preserving methods for the complexity reduction of interconnected system models. To effectively reduce high-order, interconnected mo
Externí odkaz:
http://arxiv.org/abs/2411.13344
We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a $K$-armed bandit model where some subset of $K$ arms is partitioned into $M$ groups. Within each group, the random variable associate
Externí odkaz:
http://arxiv.org/abs/2409.05072
Semantic text embedding is essential to many tasks in Natural Language Processing (NLP). While black-box models are capable of generating high-quality embeddings, their lack of interpretability limits their use in tasks that demand transparency. Rece
Externí odkaz:
http://arxiv.org/abs/2410.03435
Infrared and visible dual-modality tasks such as semantic segmentation and object detection can achieve robust performance even in extreme scenes by fusing complementary information. Most current methods design task-specific frameworks, which are lim
Externí odkaz:
http://arxiv.org/abs/2409.00973
Autor:
Sakai, Hiroyuki, Iiduka, Hideaki
This paper proposes a general framework of Riemannian adaptive optimization methods. The framework encapsulates several stochastic optimization algorithms on Riemannian manifolds and incorporates the mini-batch strategy that is often used in deep lea
Externí odkaz:
http://arxiv.org/abs/2409.00859
Probabilistic metric embedding into trees is a powerful technique for designing online algorithms. The standard approach is to embed the entire underlying metric into a tree metric and then solve the problem on the latter. The overhead in the competi
Externí odkaz:
http://arxiv.org/abs/2408.16298
One key in real-life Nash equilibrium applications is to calibrate players' cost functions. To leverage the approximation ability of neural networks, we proposed a general framework for optimizing and learning Nash equilibrium using neural networks t
Externí odkaz:
http://arxiv.org/abs/2408.16260