Browsing by Author "Kozat, S. Serdar"
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Item Open Access Handling irregularly sampled signals with gated temporal convolutional networks(2022-07-06) Aslan, Fatih; Kozat, S. SerdarWe investigate the sequential modeling problem and introduce a novel gating mechanism into the temporal convolutional network architectures. In particular, we introduce the gated temporal convolutional network architecture with elaborately tailored gating mechanisms. In our implementation, we alter the way in which the gradients flow and avoid the vanishing or exploding gradient and the dead ReLU problems. The proposed GTCN architecture is able to model the irregularly sampled sequences as well. In our experiments, we show that the basic GTCN architecture is superior to the generic TCN architectures in various benchmark tasks requiring the modeling of long-term dependencies and irregular sampling intervals. Moreover, we achieve the state-of-the-art results on the permuted sequential MNIST and the sequential CIFAR10 benchmarks with the basic structure.Item Open Access Recurrent neural networks based online learning algorithms for distributed systems(Institute of Electrical and Electronics Engineers, 2018) Ergen, Tolga; Şahin, S. Onur; Kozat, S. SerdarIn this paper, we investigate online parameter learning for Long Short Term Memory (LSTM) architectures in distributed networks. Here, we first introduce an LSTM based structure for regression. Then, we provide the equations of this structure in a state space form for each node in our network. Using this form, we then learn the parameters via our Distributed Particle Filtering based (DPF) training method. Our training method asymptotically converges to the optimal parameter set provided that we satisfy certain trivial requirements. While achieving this performance, our training method only causes a computational load that is similar to the efficient first order gradient based training methods. Through real life experiments, we show substantial performance gains compared to the conventional methods.