Search Results for author: Sercan Ö. Arik

Found 4 papers, 1 papers with code

Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

no code implementations7 Oct 2022 Rui Wang, Yihe Dong, Sercan Ö. Arik, Rose Yu

Temporal distributional shifts, with underlying dynamics changing over time, frequently occur in real-world time series, and pose a fundamental challenge for deep neural networks (DNNs).

Time Series Forecasting

Self-Supervised Learning with an Information Maximization Criterion

1 code implementation16 Sep 2022 Serdar Ozsoy, Shadi Hamdan, Sercan Ö. Arik, Deniz Yuret, Alper T. Erdogan

In this article, we argue that a straightforward application of information maximization among alternative latent representations of the same input naturally solves the collapse problem and achieves competitive empirical results.

Self-Supervised Learning

Invariant Structure Learning for Better Generalization and Causal Explainability

no code implementations13 Jun 2022 Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister

ISL splits the data into different environments, and learns a structure that is invariant to the target across different environments by imposing a consistency constraint.

Self-Supervised Learning

Interpretable Mixture of Experts for Structured Data

no code implementations5 Jun 2022 Aya Abdelsalam Ismail, Sercan Ö. Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister

We introduce a novel framework, Interpretable Mixture of Experts (IME), that provides interpretability for structured data while preserving accuracy.

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