no code implementations • 8 Feb 2024 • Bryan Perozzi, Bahare Fatemi, Dustin Zelle, Anton Tsitsulin, Mehran Kazemi, Rami Al-Rfou, Jonathan Halcrow
How can we best encode structured data into sequential form for use in large language models (LLMs)?
no code implementations • 6 Oct 2023 • Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi
Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications such as social networks, recommender systems, and computational finance.
1 code implementation • NeurIPS 2023 • Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao, Bahare Fatemi, Mike Burrows, Charith Mendis, Bryan Perozzi
TpuGraphs provides 25x more graphs than the largest graph property prediction dataset (with comparable graph sizes), and 770x larger graphs on average compared to existing performance prediction datasets on machine learning programs.
1 code implementation • 21 Aug 2023 • Bahare Fatemi, Sami Abu-El-Haija, Anton Tsitsulin, Mehran Kazemi, Dustin Zelle, Neslihan Bulut, Jonathan Halcrow, Bryan Perozzi
We implement a wide range of existing models in our framework and conduct extensive analyses of the effectiveness of different components in the framework.
1 code implementation • 15 Feb 2023 • Bahare Fatemi, Quentin Duval, Rohit Girdhar, Michal Drozdzal, Adriana Romero-Soriano
Recipe personalization through ingredient substitution has the potential to help people meet their dietary needs and preferences, avoid potential allergens, and ease culinary exploration in everyone's kitchen.
no code implementations • 14 Oct 2022 • Jonathan Pilault, Michael Galkin, Bahare Fatemi, Perouz Taslakian, David Vasquez, Christopher Pal
While using our new path-finding algorithm as a pretraining signal provides 2-3% MRR improvements, we show that pretraining on all signals together gives the best knowledge graph completion results.
1 code implementation • 18 Feb 2021 • Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole
Embedding-based methods for reasoning in knowledge hypergraphs learn a representation for each entity and relation.
1 code implementation • NeurIPS 2021 • Bahare Fatemi, Layla El Asri, Seyed Mehran Kazemi
In this work, we propose the Simultaneous Learning of Adjacency and GNN Parameters with Self-supervision, or SLAPS, a method that provides more supervision for inferring a graph structure through self-supervision.
Ranked #1 on Graph structure learning on Cora
1 code implementation • 1 Jun 2019 • Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole
Knowledge graphs store facts using relations between two entities.
no code implementations • 7 Dec 2018 • Bahare Fatemi, Siamak Ravanbakhsh, David Poole
Knowledge graphs are used to represent relational information in terms of triples.
no code implementations • 6 Aug 2018 • Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahare Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting, Sriraam Natarajan
We consider the problem of learning Relational Logistic Regression (RLR).
no code implementations • 26 Jun 2018 • Bahare Fatemi, Seyed Mehran Kazemi, David Poole
We provide a probabilistic model using relational logistic regression to find the probability of each record in the database being the desired record for a given query and find the best record(s) with respect to the probabilities.
no code implementations • 25 Jul 2017 • Seyed Mehran Kazemi, Bahare Fatemi, Alexandra Kim, Zilun Peng, Moumita Roy Tora, Xing Zeng, Matthew Dirks, David Poole
Relational probabilistic models have the challenge of aggregation, where one variable depends on a population of other variables.
no code implementations • 28 Jun 2016 • Bahare Fatemi, Seyed Mehran Kazemi, David Poole
We compare our learning algorithm to other structure and parameter learning algorithms in the literature, and compare the performance of RLR models to standard logistic regression and RDN-Boost on a modified version of the MovieLens data-set.