Search Results for author: Çağatay Yıldız

Found 6 papers, 5 papers with code

Modulated Neural ODEs

1 code implementation NeurIPS 2023 Ilze Amanda Auzina, Çağatay Yıldız, Sara Magliacane, Matthias Bethge, Efstratios Gavves

Neural ordinary differential equations (NODEs) have been proven useful for learning non-linear dynamics of arbitrary trajectories.

Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs

1 code implementation24 May 2022 Çağatay Yıldız, Melih Kandemir, Barbara Rakitsch

We study time uncertainty-aware modeling of continuous-time dynamics of interacting objects.


Variational multiple shooting for Bayesian ODEs with Gaussian processes

1 code implementation21 Jun 2021 Pashupati Hegde, Çağatay Yıldız, Harri Lähdesmäki, Samuel Kaski, Markus Heinonen

Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data.

Bayesian Inference Gaussian Processes +1

Continuous-Time Model-Based Reinforcement Learning

1 code implementation9 Feb 2021 Çağatay Yıldız, Markus Heinonen, Harri Lähdesmäki

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.

Model-based Reinforcement Learning reinforcement-learning +1

ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks

1 code implementation27 May 2019 Çağatay Yıldız, Markus Heinonen, Harri Lähdesmäki

We present Ordinary Differential Equation Variational Auto-Encoder (ODE$^2$VAE), a latent second order ODE model for high-dimensional sequential data.

Imputation motion prediction +3

Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization

no code implementations ICML 2018 Umut Şimşekli, Çağatay Yıldız, Thanh Huy Nguyen, Gaël Richard, A. Taylan Cemgil

The results support our theory and show that the proposed algorithm provides a significant speedup over the recently proposed synchronous distributed L-BFGS algorithm.

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