Cagatay Yildiz

Cagatay Yildiz
Aalto University · Department of Computer Science

Master of Science

About

12
Publications
761
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62
Citations
Introduction
Skills and Expertise

Publications

Publications (12)
Preprint
Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data. However, earlier works are based on approximative ODE solutions or point estimates. We propose a novel Bayesian nonparametric model that uses Gaussian processes to infer posteriors of unknown ODE systems directly from d...
Preprint
Model-based reinforcement learning (MBRL) approaches rely on discrete-time state transition models whereas physical systems and the vast majority of control tasks operate in continuous-time. To avoid time-discretization approximation of the underlying process, we propose a continuous-time MBRL framework based on a novel actor-critic method. Our app...
Preprint
We present Ordinary Differential Equation Variational Auto-Encoder (ODE$^2$VAE), a latent second order ODE model for high-dimensional sequential data. Leveraging the advances in deep generative models, ODE$^2$VAE can simultaneously learn the embedding of high dimensional trajectories and infer arbitrarily complex continuous-time latent dynamics. Ou...
Preprint
We introduce a novel paradigm for learning non-parametric drift and diffusion functions for stochastic differential equation (SDE) that are learnt to simulate trajectory distributions that match observations of arbitrary spacings. This is in contrast to existing gradient matching or other approximations that do not optimize simulated responses. We...
Preprint
Recent studies have illustrated that stochastic gradient Markov Chain Monte Carlo techniques have a strong potential in non-convex optimization, where local and global convergence guarantees can be shown under certain conditions. By building up on this recent theory, in this study, we develop an asynchronous-parallel stochastic L-BFGS algorithm for...
Article
In conventional ODE modelling coefficients of an equation driving the system state forward in time are estimated. However, for many complex systems it is practically impossible to determine the equations or interactions governing the underlying dynamics. In these settings, parametric ODE model cannot be formulated. Here, we overcome this issue by i...
Article
Full-text available
In this work we present a real time SIP network simulation and monitoring system. The SIP network simulator is based on a probabilistic generative model that mimics a social network of VoIP subscribers calling each other at random times. The monitoring system, installed at a SIP server, provides services for collecting network data and server stati...
Article
Session Initiation Protocol (SIP), as one the most common signaling mechanism for Voice Over Internet Protocol (VoIP) applications, is a popular target for the flooding-based Distributed Denial of Service (DDoS) attacks. In this paper, we propose a DDoS attack detection framework based on the Bayesian multiple change model, which can detect differe...
Conference Paper
Full-text available
Epilepsy is a chronic neurological disorder in which the normal pattern of neuronal activity in the brain becomes disturbed. Identification of the brain region that is abnormally active during an epileptic seizure is vital for epilepsy surgery. One way of achieving so is to collect electroencephalography (EEG) signals from epileptic people and then...
Conference Paper
Experimenting with large-scale real world data is crucial for the development of network protocol and investigate their performance. However, collecting such data from real networks, and especially to annotate them with ground truth proves to, if not impossible, too tedious. In such cases use of simulated data, generated for various network scenari...

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