Zobrazeno 1 - 10
of 6 188
pro vyhledávání: '"Ergen, A."'
Large Language Models (LLMs) have shown impressive capabilities in many scenarios, but their performance depends, in part, on the choice of prompt. Past research has focused on optimizing prompts specific to a task. However, much less attention has b
Externí odkaz:
http://arxiv.org/abs/2410.14826
Autor:
Ergen, Mert, Arık, Metin
We construct a simple model of a closed FLRW spacetime by starting from a flat five dimensional scalar field space with a quartic potential. The action contains no curvature terms. The spacetime metric is uniquely determined from the dynamical equati
Externí odkaz:
http://arxiv.org/abs/2410.12505
Effective diabetes management relies heavily on the continuous monitoring of blood glucose levels, traditionally achieved through invasive and uncomfortable methods. While various non-invasive techniques have been explored, such as optical, microwave
Externí odkaz:
http://arxiv.org/abs/2408.08109
Autor:
Zhang, Xingjian, Xie, Yutong, Huang, Jin, Ma, Jinge, Pan, Zhaoying, Liu, Qijia, Xiong, Ziyang, Ergen, Tolga, Shim, Dongsub, Lee, Honglak, Mei, Qiaozhu
Scientific innovation relies on detailed workflows, which include critical steps such as analyzing literature, generating ideas, validating these ideas, interpreting results, and inspiring follow-up research. However, scientific publications that doc
Externí odkaz:
http://arxiv.org/abs/2406.06357
We prove that training neural networks on 1-D data is equivalent to solving convex Lasso problems with discrete, explicitly defined dictionary matrices. We consider neural networks with piecewise linear activations and depths ranging from 2 to an arb
Externí odkaz:
http://arxiv.org/abs/2403.01046
Autor:
Bosman, Thomas, van Ee, Martijn, Ergen, Ekin, Imreh, Csanad, Marchetti-Spaccamela, Alberto, Skutella, Martin, Stougie, Leen
Scheduling jobs with given processing times on identical parallel machines so as to minimize their total completion time is one of the most basic scheduling problems. We study interesting generalizations of this classical problem involving scenarios.
Externí odkaz:
http://arxiv.org/abs/2402.19259
Autor:
Ahmadli, Nihat, Sarsil, Mehmet Ali, Mizrak, Berk, Karauzum, Kurtulus, Shaker, Ata, Tulumen, Erol, Mirzamidinov, Didar, Ural, Dilek, Ergen, Onur
Addressing heart failure (HF) as a prevalent global health concern poses difficulties in implementing innovative approaches for enhanced patient care. Predicting mortality rates in HF patients, in particular, is difficult yet critical, necessitating
Externí odkaz:
http://arxiv.org/abs/2402.13812
Autor:
Ergen, Tolga, Pilanci, Mert
Due to the non-convex nature of training Deep Neural Network (DNN) models, their effectiveness relies on the use of non-convex optimization heuristics. Traditional methods for training DNNs often require costly empirical methods to produce successful
Externí odkaz:
http://arxiv.org/abs/2312.12657
Autor:
Pekaslan, Direnc, Alonso-Moral, Jose Maria, Bandara, Kasun, Bergmeir, Christoph, Bernabe-Moreno, Juan, Eigenmann, Robert, Einecke, Nils, Ergen, Selvi, Godahewa, Rakshitha, Hewamalage, Hansika, Lago, Jesus, Limmer, Steffen, Rebhan, Sven, Rabinovich, Boris, Rajapasksha, Dilini, Song, Heda, Wagner, Christian, Wu, Wenlong, Magdalena, Luis, Triguero, Isaac
This paper presents the real-world smart-meter dataset and offers an analysis of solutions derived from the Energy Prediction Technical Challenges, focusing primarily on two key competitions: the IEEE Computational Intelligence Society (IEEE-CIS) Tec
Externí odkaz:
http://arxiv.org/abs/2311.04007
Autor:
Ergen, Ekin, Grillo, Moritz
We study the expressivity of ReLU neural networks in the setting of a binary classification problem from a topological perspective. Recently, empirical studies showed that neural networks operate by changing topology, transforming a topologically com
Externí odkaz:
http://arxiv.org/abs/2310.11130