Zobrazeno 1 - 10
of 132
pro vyhledávání: '"Kozat, Suleyman Serdar"'
Traditional machine learning approaches assume that data comes from a single generating mechanism, which may not hold for most real life data. In these cases, the single mechanism assumption can result in suboptimal performance. We introduce a cluste
Externí odkaz:
http://arxiv.org/abs/2411.06572
We investigate ensemble methods for prediction in an online setting. Unlike all the literature in ensembling, for the first time, we introduce a new approach using a meta learner that effectively combines the base model predictions via using a supers
Externí odkaz:
http://arxiv.org/abs/2211.16884
Existing approaches to the crime prediction problem are unsuccessful in expressing the details since they assign the probability values to large regions. This paper introduces a new architecture with the graph convolutional networks (GCN) and multiva
Externí odkaz:
http://arxiv.org/abs/2111.14733
Approximation of the value functions in value-based deep reinforcement learning induces overestimation bias, resulting in suboptimal policies. We show that when the reinforcement signals received by the agents have a high variance, deep actor-critic
Externí odkaz:
http://arxiv.org/abs/2109.11788
Numerical Weather Forecasting using Convolutional-LSTM with Attention and Context Matcher Mechanisms
Numerical weather forecasting using high-resolution physical models often requires extensive computational resources on supercomputers, which diminishes their wide usage in most real-life applications. As a remedy, applying deep learning methods has
Externí odkaz:
http://arxiv.org/abs/2102.00696
We investigate nonlinear regression for nonstationary sequential data. In most real-life applications such as business domains including finance, retail, energy and economy, timeseries data exhibits nonstationarity due to the temporally varying dynam
Externí odkaz:
http://arxiv.org/abs/2006.10119
We study anomaly detection and introduce an algorithm that processes variable length, irregularly sampled sequences or sequences with missing values. Our algorithm is fully unsupervised, however, can be readily extended to supervised or semisupervise
Externí odkaz:
http://arxiv.org/abs/2005.12005
We study the spatio-temporal prediction problem, which has attracted the attention of many researchers due to its critical real-life applications. In particular, we introduce a novel approach to this problem. Our approach is based on the Hawkes proce
Externí odkaz:
http://arxiv.org/abs/2003.03657
Autor:
Ilhan, Fatih, Kozat, Suleyman Serdar
We investigate spatio-temporal event analysis using point processes. Inferring the dynamics of event sequences spatiotemporally has many practical applications including crime prediction, social media analysis, and traffic forecasting. In particular,
Externí odkaz:
http://arxiv.org/abs/2003.03671
We study the min-max optimization problem where each function contributing to the max operation is strongly-convex and smooth with bounded gradient in the search domain. By smoothing the max operator, we show the ability to achieve an arbitrarily sma
Externí odkaz:
http://arxiv.org/abs/1905.12733