no code implementations • 27 Feb 2024 • Çağatay Yıldız, Nishaanth Kanna Ravichandran, Prishruit Punia, Matthias Bethge, Beyza Ermis
This paper studies the evolving domain of Continual Learning (CL) in large language models (LLMs), with a focus on developing strategies for efficient and sustainable training.
1 code implementation • 27 Dec 2023 • Sebastian Dziadzio, Çağatay Yıldız, Gido M. van de Ven, Tomasz Trzciński, Tinne Tuytelaars, Matthias Bethge
In a simple setting with direct supervision on the generative factors, we show how learning class-agnostic transformations offers a way to circumvent catastrophic forgetting and improve classification accuracy over time.
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.
1 code implementation • 24 May 2022 • Çağatay Yıldız, Melih Kandemir, Barbara Rakitsch
We study time uncertainty-aware modeling of continuous-time dynamics of interacting objects.
1 code implementation • 21 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.
1 code implementation • 9 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
1 code implementation • 27 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.
Ranked #1 on Video Prediction on CMU Mocap-1
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.