Berker Demirel

I am a PhD candidate at Causal Learning and Artificial Intelligence Lab, IST Austria supervised by Francesco Locatello. My research interests lie in the domains of domain generalization and out-of-distribution generalization, where statistical and information-theoretical interpretations of the models are emphasized.

Prior to beginning my PhD, I earned a master's degree from Sabanci University. During this time, I was a member of the VPA Lab and was under the supervision of Asst. Prof. Huseyin Ozkan and Asst. Prof. Erchan Aptoula. The primary focus of my master's studies was on group activity recognition and domain generalization.

In the summer of 2019, I completed a summer internship at Erasmus University Rotterdam.

Email  /  CV  /  GitHub  /  Google Scholar  /  Twitter

profile photo
Publications and Projects
Look Around and Find Out: OOD Detection with Relative Angles
Berker Demirel, Marco Fumero, Francesco Locatello
arXiv:2410.04525, 2024
pdf / code / bib

We present a novel technique -Look Around and Find Out (LAFO)- for OOD detection, which computes the angles between the feature representation and its projection to the decision boundaries, relative to the mean of ID-features. LAFO is model-agnostic, hyperparameter-free, and efficient, scaling linearly with the number of ID-classes. Therefore, it can flexibly be combined with various architectures without the need for additional tuning. In addition, the scale-invariant property of LAFO allows for straightforward aggregation of confidence scores from multiple pre-trained models, improving ensemble performance for OOD detection.

Adjusting Pretrained Backbones for Performativity
Berker Demirel, Lingjing Kong, Kun Zhang, Theofanis Karaletsos, Celestine Mendler-Dünner, Francesco Locatello
arXiv:2410.04499, 2024
pdf / code / bib

We propose a modular approach to tackle performative label shift for pretrained backbones. This additional module serves two main use cases: (i) adapting the model for performative shift and (ii) making informed model selection by anticipating future distributions caused by multiple models. For the first use case, it allows pre-shift adaptation for networks to better handle performative shifts. For the second, it can anticipate a model's robustness to performative shifts, enabling more informative model selection. Thanks to our modeling approach capturing the inherent relationship between the sufficient statistic and the performative shift, it is not coupled with the specific architecture it is trained with. Therefore, it can seamlessly combine with various pretrained networks, allowing zero-shot transfer during model updates.

DECOMPL: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball Image
Berker Demirel, Huseyin Ozkan
IEEE International Conference on Image Processing (ICIP), 2024
pdf / code / bib

We show that Group Activity Recognition Problem can be formulated using Attention Pooling mechanism and can perform on par with the other state-of-the-art methods even with a single RGB frame. Moreover, we manually reannotated the flawed instances in the Volleyball Dataset, which is one of the widely used datasets in Group Activity Recognition.

ADRMX: Additive Disentanglement of Domain Features with Remix Loss
Berker Demirel, Erchan Aptoula, Huseyin Ozkan
arXiv:2308.06624, 2023
pdf / code / bib

We propose an additive disentanglement of domain specific and domain invariant features for the domain generalization problem. Unlike prior work, we demonstrate the potential benefits of utilizing domain specific features along with domain invariant ones. Moreover, we introduce a new data augmentation technique to enhance the generalization capacity of the architecture, where samples from different domains are mixed within the latent space.

Vertex Ordering Algorithms for Graph Coloring Problem
Berker Demirel, Arda Asik, Bugra Demir, Kamer Kaya, Baris Batuhan Topal
IEEE Signal Processing and Communications Applications (SIU), 2020
pdf / code / bib

We developed a ranking algorithm that uses metrics of degree 1/2/3, closeness centrality, clustering coefficient, and page rank to rank the nodes in a graph. Then, we applied greedy coloring to the graph using the ranking as a guide, which resulted in significantly better colorings. Furthermore, we tried to extend this idea using a model-free policy based reinforcement learning algorithm while parallelizing C++ backend using OpenMP.

Implementation of a small GPT from scratch
Berker Demirel
pdf / code

I re-implemented Andrej Karpathy's nanoGPT. It is designed to be simpler and easier to update. Performance evaluation is performed on Tiny Shakespeare Dataset and results can be found in the repository.

Implementation of UMAP in Python
Berker Demirel
pdf / code

I implemented Uniform Manifold Approximation and Projection (UMAP) algorithm in Python from scratch and performed experiments on MNIST and Load Digits datasets.

Implementation of Direction-Optimized Breadth First Search using OpenMP and CUDA (Course Project)
Berker Demirel, Naci Ege Sarac
pdf / code

We implemented Direction-Optimized BFS using OpenMP and CUDA.


Website template credits to Jon Barron.