Emre Akbas

DeepKin: Predicting Relatedness From Low‐Coverage Genomes and Palaeogenomes With Convolutional Neural Networks DeepKin is a novel tool designed to predict relatedness from genomic data using convolutional neural networks (CNNs). Traditional methods for estimating relatedness often struggle when genomic data i...

Our genetic kinship estimation tool “DeepKin” is now available! Our neural network models trained on simulated data work effectively on real ancient data from diverse backgrounds and often outperform available tools. @compevohumang.bsky.social onlinelibrary.wiley.com/doi/10.1111/...

Postdoc fellowship opportunities at METU: enrichtogether.metu.edu.tr/faqs/ for 24 months, good compensation.

NeurIPS participation in Europe We seek to understand if there is interest in being able to attend NeurIPS in Europe, i.e. without travelling to San Diego, US. In the following, assume that it is possible to present accepted papers ...

Would you present your next NeurIPS paper in Europe instead of traveling to San Diego (US) if this was an option? Søren Hauberg (DTU) and I would love to hear the answer through this poll: (1/6)

The companion website is open to all at 384book.net.

Book can be found at www.wiley.com/en-us/Signal...

Wiley did an excellent job creating the e-book version. See the attached video for an excerpt! (The ebook is hosted at vitalsource.com)

Over the years of teaching this course with Prof. Fatos Yarman Vural, we have gradually enriched and transformed her handwritten lecture notes into a textbook. It has been a long and challenging but fun project!

🚀 New Textbook Announcement! 📖

Dear fellow academics, if you're teaching an undergraduate Signals and Systems course, consider using our new textbook: "Signals and Systems: Theory and Practical Explorations with Python".

Dear @csprofkgd.bsky.social , I'd love to be included if there is space. Thank you!

A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection We propose average Localisation-Recall-Precision (aLRP), a unified, bounded, balanced and ranking-based loss function for both classification and localisation tasks in object detection. aLRP extends t...

When you want to directly optimize the evaluation measure, e.g. in object detection, you face non-differentiable or zero-gradient components. Then, you manipulate the gradient, see: aLRP loss arxiv.org/abs/2009.13592 and RS loss: arxiv.org/abs/2107.11669.