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7 de may. de 2024 · Talk Like a Graph: Encoding Graphs for Large Language Models (see blog post) Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi. When Scaling Meets LLM Fine-Tuning: The Effect of Data, Model and Fine-Tuning Methods Biao Zhang, Zhongtao Liu, Colin Cherry, Orhan Firat
Hace 2 días · [16] Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao, Bahare Fatemi, Michael Burrows, Charith Mendis, and Bryan Perozzi. Tpugraphs: A performance prediction dataset on large tensor computational graphs. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2023.
10 de may. de 2024 · [25] Bahare Fatemi, Jonathan Halcrow, and Bryan Perozzi. Talk like a graph: Encoding graphs for large language models. In The Twelfth International Conference on Learning Representations, 2024. [26] Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael Bronstein, Zhaocheng Zhu, and Jian Tang. Graphtext: Graph reasoning in text ...
2 de may. de 2024 · Abstract. Representation learning has been instrumental in the success of machine learning, offering compact and performant data representations for diverse downstream tasks. In the spatial domain, it has been pivotal in extracting latent patterns from various data types, including points, polylines, polygons, and networked structures.
17 de may. de 2024 · Abu-El-Haija et al. (2019) Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019. Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In international conference on machine learning, pages 21–29. PMLR.
17 de may. de 2024 · [26] Perozzi Bryan, Al-Rfou Rami, and Skiena Steven. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 701 – 710. Google Scholar Digital Library [27] Pham Phu and Do Phuc. 2019.
13 de may. de 2024 · TLDR. Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds. Expand. 15,084.
