Yearly Archives: 2024

Open-Rank Position, Department of Neuroscience at Bilkent University

Bilkent University invites applications for multiple open-rank faculty positions in the Department of Neuroscience. The department plans to expand research activities in certain focus areas and accordingly seeks applications from promising or established scholars who have worked in the following or related fields: Computational/theoretical neuroscience with a strong emphasis on research involving emerging approaches including […]

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Sememe Based Semantic Communications

Happy to share our latest paper titled “Sememe Based Semantic Communications” published in IEEE Communications Letters! We introduced a concept in linguistics called sememes to the domain of semantic communications. Sememes are the smallest and indivisible semantic units of word meaning. A predefined set of sememes is theoretically considered “the periodic table” of meaning in […]

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VISPool: Enhancing Transformer Encoders with Vector Visibility Graph Neural Networks

We are happy to announce that we demonstrated our latest work titled “VISPool: Enhancing Transformer Encoders with Vector Visibility Graph Neural Networks” at ACL 2024, Bangkok/Thailand!   In our #ACL2024 paper, we explore the powerful combination of transformers and graph neural networks (GNNs) to push the boundaries of natural language processing (NLP). While transformers have […]

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Graph Fractional Fourier Transform: A Unified Theory

We are pleased to announce that our paper on generalizing the fractional Fourier transform to the graph domain titled “Graph Fractional Fourier Transform: A Unified Theory” published in IEEE Transactions on Signal Processing! Highlights of our contributions include: A rigorous extension of the fractional power-based definition of GFRFT to support any graph structure and transform […]

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Text-RGNNs: Relational Modeling for Heterogeneous Text Graphs

Happy to share our latest paper titled “Text-RGNNs: Relational Modeling for Heterogeneous Text Graphs” published in IEEE Signal Processing Letters! Building on the foundational Text-graph convolutional Network (TextGCN), which represents corpus with heterogeneous text graphs, we addressed a key limitation: GCNs are inherently designed to operate within homogeneous graphs, potentially limiting their performance. To overcome […]

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