Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Sanchez, Ramses J."'
Markov jump processes are continuous-time stochastic processes which describe dynamical systems evolving in discrete state spaces. These processes find wide application in the natural sciences and machine learning, but their inference is known to be
Externí odkaz:
http://arxiv.org/abs/2406.06419
Dynamical systems governed by ordinary differential equations (ODEs) serve as models for a vast number of natural and social phenomena. In this work, we offer a fresh perspective on the classical problem of imputing missing time series data, whose un
Externí odkaz:
http://arxiv.org/abs/2402.07594
Autor:
Seifner, Patrick, Sanchez, Ramses J.
Markov jump processes are continuous-time stochastic processes with a wide range of applications in both natural and social sciences. Despite their widespread use, inference in these models is highly non-trivial and typically proceeds via either Mont
Externí odkaz:
http://arxiv.org/abs/2305.19744
Topic models and all their variants analyse text by learning meaningful representations through word co-occurrences. As pointed out by Williamson et al. (2010), such models implicitly assume that the probability of a topic to be active and its propor
Externí odkaz:
http://arxiv.org/abs/2301.10988
Large pre-trained language models (LPLM) have shown spectacular success when fine-tuned on downstream supervised tasks. Yet, it is known that their performance can drastically drop when there is a distribution shift between the data used during train
Externí odkaz:
http://arxiv.org/abs/2211.00384
Large, pretrained language models infer powerful representations that encode rich semantic and syntactic content, albeit implicitly. In this work we introduce a novel neural language model that enforces, via inductive biases, explicit relational stru
Externí odkaz:
http://arxiv.org/abs/2207.03777
Just as user preferences change with time, item reviews also reflect those same preference changes. In a nutshell, if one is to sequentially incorporate review content knowledge into recommender systems, one is naturally led to dynamical models of te
Externí odkaz:
http://arxiv.org/abs/2110.14747
Publikováno v:
2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, 2020, pp. 1-8
Deep neural network models represent the state-of-the-art methodologies for natural language processing. Here we build on top of these methodologies to incorporate temporal information and model how to review data changes with time. Specifically, we
Externí odkaz:
http://arxiv.org/abs/2012.05684
Autor:
Cvejoski, Kostadin, Sanchez, Ramses J., Georgiev, Bogdan, Schuecker, Jannis, Bauckhage, Christian, Ojeda, Cesar
Recent progress in recommender system research has shown the importance of including temporal representations to improve interpretability and performance. Here, we incorporate temporal representations in continuous time via recurrent point process fo
Externí odkaz:
http://arxiv.org/abs/1912.04132
Autor:
Ojeda, César, Cvejosky, Kostadin, Sánchez, Ramsés J., Schuecker, Jannis, Georgiev, Bogdan, Bauckhage, Christian
Service system dynamics occur at the interplay between customer behaviour and a service provider's response. This kind of dynamics can effectively be modeled within the framework of queuing theory where customers' arrivals are described by point proc
Externí odkaz:
http://arxiv.org/abs/1906.09808