publications

2022

1. ICLR

   Evidential Turing Processes

   M. Kandemir, \textbfAbdullah. \textbfAkgül, M. Haussmann, and 1 more author

   In International Conference on Learning Representations 2022

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   A probabilistic classifier with reliable predictive uncertainties i) fits successfully to the target domain data, ii) provides calibrated class probabilities in difficult regions of the target domain (e.g. class overlap), and iii) accurately identifies queries coming out of the target domain and reject them. We introduce an original combination of Evidential Deep Learning, Neural Processes, and Neural Turing Machines capable of providing all three essential properties mentioned above for total uncertainty quantification. We observe our method on three image classification benchmarks to consistently improve the in-domain uncertainty quantification, out-of-domain detection, and robustness against input perturbations with one single model. Our unified solution delivers an implementation-friendly and computationally efficient recipe for safety clearance and provides intellectual economy to an investigation of algorithmic roots of epistemic awareness in deep neural nets.

   @inproceedings{kandemir2022evidential,
     title = {Evidential Turing Processes },
     author = {Kandemir, M. and \textbf{Akgül}, \textbf{Abdullah}. and Haussmann, M. and Unal, G.},
     booktitle = {International Conference on Learning Representations},
     year = {2022},
     url = {https://openreview.net/forum?id=84NMXTHYe-},
   }

2. arXiv

   Continual Learning of Multi-modal Dynamics with External Memory

   \textbfAbdullah. \textbfAkgül, G. Unal, and M. Kandemir

   In arXiv Preprint 2022

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   We study the problem of fitting a model to a dynamical environment when new modes of behavior emerge sequentially. The learning model is aware when a new mode appears, but it does not have access to the true modes of individual training sequences. We devise a novel continual learning method that maintains a descriptor of the mode of an encountered sequence in a neural episodic memory. We employ a Dirichlet Process prior on the attention weights of the memory to foster efficient storage of the mode descriptors. Our method performs continual learning by transferring knowledge across tasks by retrieving the descriptors of similar modes of past tasks to the mode of a current sequence and feeding this descriptor into its transition kernel as control input. We observe the continual learning performance of our method to compare favorably to the mainstream parameter transfer approach.

   @inproceedings{akgul2022cddp,
     title = {Continual Learning of Multi-modal Dynamics with External Memory},
     author = {\textbf{Akgül}, \textbf{Abdullah}. and Unal, G. and Kandemir, M.},
     booktitle = {arXiv Preprint},
     year = {2022},
   }

3. FL-NeurIPS

   How to Combine Variational Bayesian Networks in Federated Learning

   A. Ozer, K.B. Buldu, \textbfAbdullah. \textbfAkgül, and 1 more author

   In Workshop on Federated Learning: Recent Advances and New Challenges (in Conjunction with NeurIPS 2022) 2022

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   Federated Learning enables multiple data centers to train a central model collaboratively without exposing any confidential data. Even though deterministic models are capable of performing high prediction accuracy, their lack of calibration and capability to quantify uncertainty is problematic for safety-critical applications. Different from deterministic models, probabilistic models such as Bayesian neural networks are relatively well-calibrated and able to quantify uncertainty alongside their competitive prediction accuracy. Both of the approaches appear in the federated learning framework; however, the aggregation scheme of deterministic models cannot be directly applied to probabilistic models since weights correspond to distributions instead of point estimates. In this work, we study the effects of various aggregation schemes for variational Bayesian neural networks. With empirical results on three image classification datasets, we observe that the degree of spread for an aggregated distribution is a significant factor in the learning process. Hence, we present an survey on the question of how to combine variational Bayesian networks in federated learning, while providing computer vision classification benchmarks for different aggregation settings.

   @inproceedings{ozer2022fl,
     title = {How to Combine Variational Bayesian Networks in Federated Learning},
     author = {Ozer, A. and Buldu, K.B. and \textbf{Akgül}, \textbf{Abdullah}. and Unal, G.},
     booktitle = {Workshop on Federated Learning: Recent Advances and New Challenges (in Conjunction with NeurIPS 2022)},
     year = {2022},
     url = {https://openreview.net/forum?id=AkPwb9dvAlP},
   }