arXiv subjects

Ercan Engin Kuruoğlu

Publications and source records attributed to Ercan Engin Kuruoğlu.

3 recordsLinked to original sources

arXiv2025

Sequential Function-Space Variational Inference via Gaussian Mixture Approximation

Continual learning in neural networks aims to learn new tasks without forgetting old tasks. Sequential function-space variational inference (SFSVI) uses a Gaussian variational distribution to approximate the distribution of the outputs of the neural network corresponding to a finite number of selected inducing points. Since the posterior distribution of a neural network is multi-modal, a Gaussian distribution could only match one mode of the posterior distribution, and a Gaussian mixture distribution could be used to better approximate the posterior distribution. We propose an SFSVI method based on a Gaussian mixture variational distribution. We also compare different types of variational inference methods with a fixed pre-trained feature extractor (where continual learning is performed on the final layer) and without a fixed pre-trained feature extractor (where continual learning is performed on all layers). We find that in terms of final average accuracy, likelihood-focused Gaussian mixture SFSVI outperforms other sequential variational inference methods, especially in the latter case.

cs.LG↗

arXiv2025

On Sequential Maximum a Posteriori Inference for Continual Learning

We formulate sequential maximum a posteriori inference as a recursion of loss functions and reduce the problem of continual learning to approximating the previous loss function. We then propose two coreset-free methods: autodiff quadratic consolidation, which uses an accurate and full quadratic approximation, and neural consolidation, which uses a neural network approximation. These methods are not scalable with respect to the neural network size, and we study them for classification tasks in combination with a fixed pre-trained feature extractor. We also introduce simple but challenging classical task sequences based on Iris and Wine datasets. We find that neural consolidation performs well in the classical task sequences, where the input dimension is small, while autodiff quadratic consolidation performs consistently well in image task sequences with a fixed pre-trained feature extractor, achieving comparable performance to joint maximum a posteriori training in many cases.

cs.LG↗

arXiv2023

Robustness Enhancement in Neural Networks with Alpha-Stable Training Noise

With the increasing use of deep learning on data collected by non-perfect sensors and in non-perfect environments, the robustness of deep learning systems has become an important issue. A common approach for obtaining robustness to noise has been to train deep learning systems with data augmented with Gaussian noise. In this work, we challenge the common choice of Gaussian noise and explore the possibility of stronger robustness for non-Gaussian impulsive noise, specifically alpha-stable noise. Justified by the Generalized Central Limit Theorem and evidenced by observations in various application areas, alpha-stable noise is widely present in nature. By comparing the testing accuracy of models trained with Gaussian noise and alpha-stable noise on data corrupted by different noise, we find that training with alpha-stable noise is more effective than Gaussian noise, especially when the dataset is corrupted by impulsive noise, thus improving the robustness of the model. The generality of this conclusion is validated through experiments conducted on various deep learning models with image and time series datasets, and other benchmark corrupted datasets. Consequently, we propose a novel data augmentation method that replaces Gaussian noise, which is typically added to the training data, with alpha-stable noise.

cs.LG↗