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Posts

Building Blocks for Deep Learning: Crafting an MLP with Triton

14 minute read

Published: December 26, 2024

In our previous post, we dipped our toes into the world of Triton by writing simple custom kernels. We learned how to add a scalar to a tensor and perform element-wise multiplication, understanding the fundamental concepts of launching kernels and managing GPU memory. Read more

Building Blocks for Deep Learning: Your First Custom Kernel with Triton

14 minute read

Published: December 25, 2024

So, you’ve heard the buzz about OpenAI’s Triton and its ability to let you craft custom GPU kernels? Maybe you’re facing performance bottlenecks with standard deep learning operations, or perhaps you have a unique algorithm that existing libraries don’t quite cover. Whatever your reason, you’re in the right place! This winter break, I decided to dive into the fascinating world of high-performance GPU programming, and one of the most exciting tools I’ve discovered is Triton. Read more

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publications

Syntax-Guided Transformers: Elevating Compositional Generalization and Grounding in Multimodal Environments

The Conference on Empirical Methods in Natural Language Processing Genbench Workshop (EMNLP), 2023

In this work, we proposed the syntax guided transformer to improve the compositional generalization in grounding. Read more

Recommended citation: Kamali, Danial, and Parisa Kordjamshidi. "Syntax-Guided Transformers: Elevating Compositional Generalization and Grounding in Multimodal Environments." GenBench: The first workshop on generalisation (benchmarking) in NLP. 2023. https://aclanthology.org/2023.genbench-1.10.pdf

        @inproceedings{kamali2023syntax \n ,title={Syntax-Guided Transformers: Elevating Compositional Generalization and Grounding in Multimodal Environments} \n,author={Kamali, Danial and Kordjamshidi, Parisa} \n,booktitle={GenBench: The first workshop on generalisation (benchmarking) in NLP}\n,pages={130}\n,year={2023}\n}

Using Persuasive Writing Strategies to Explain and Detect Health Misinformation

Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING), 2024

In this work we introduce a persuasive strategy detection dataset and show using their labels can improve misinformation detection and explanation. Read more

Recommended citation: Kamali, D., Romain, J., Liu, H., Peng, W., Meng, J., & Kordjamshidi, P. (2023). Using Persuasive Writing Strategies to Explain and Detect Health Misinformation. arXiv preprint arXiv:2211.05985. https://aclanthology.org/2024.lrec-main.1501.pdf

        @inproceedings{kamali-etal-2024-using, title = "Using Persuasive Writing Strategies to Explain and Detect Health Misinformation", author = "Kamali, Danial  and Romain, Joseph D.  and Liu, Huiyi  and Peng, Wei  and Meng, Jingbo  and Kordjamshidi, Parisa", editor = "Calzolari, Nicoletta  and Kan, Min-Yen  and Hoste, Veronique  and Lenci, Alessandro  and Sakti, Sakriani  and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.1501", pages = "17285--17309", }

NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional Generalization

Association for the Advancement of Artificial Intelligence (AAAI), 2025

Abstract

NeSyCoCo is a neuro-symbolic visual reasoning framework that tackles generalization in vision-language tasks and more specially compositional generalization. Read more

         @misc{kamali2024nesycoconeurosymbolicconceptcomposer, title={NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional Generalization}, author={Danial Kamali and Elham J. Barezi and Parisa Kordjamshidi}, year={2024}, eprint={2412.15588}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.15588}, }