1 code implementation • NeurIPS 2023 • Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Seyed Mehran Kazemi, Najoung Kim, He He
Given the intractably large size of the space of proofs, any model that is capable of general deductive reasoning must generalize to proofs of greater complexity.
no code implementations • 12 Oct 2022 • Aditya Sharma, Apoorv Saxena, Chitrank Gupta, Seyed Mehran Kazemi, Partha Talukdar, Soumen Chakrabarti
Recent years have witnessed much interest in temporal reasoning over knowledge graphs (KG) for complex question answering (QA), but there remains a substantial gap in human capabilities.
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
no code implementations • Findings of the Association for Computational Linguistics 2020 • Marjan Albooyeh, Rishab Goel, Seyed Mehran Kazemi
Many important problems can be formulated as reasoning in knowledge graphs.
1 code implementation • 28 Apr 2020 • Marjan Albooyeh, Rishab Goel, Seyed Mehran Kazemi
Many important problems can be formulated as reasoning in knowledge graphs.
6 code implementations • 11 Jul 2019 • Seyed Mehran Kazemi, Rishab Goel, Sepehr Eghbali, Janahan Ramanan, Jaspreet Sahota, Sanjay Thakur, Stella Wu, Cathal Smyth, Pascal Poupart, Marcus Brubaker
Time is an important feature in many applications involving events that occur synchronously and/or asynchronously.
2 code implementations • 6 Jul 2019 • Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, Pascal Poupart
In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time.
no code implementations • 27 May 2019 • Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart
Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance.
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.
2 code implementations • NeurIPS 2018 • Seyed Mehran Kazemi, David Poole
We prove SimplE is fully expressive and derive a bound on the size of its embeddings for full expressivity.
Ranked #19 on Link Prediction on FB15k
1 code implementation • 7 Dec 2017 • Seyed Mehran Kazemi, David Poole
Statistical relational AI (StarAI) aims at reasoning and learning in noisy domains described in terms of objects and relationships by combining probability with first-order logic.
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 • 24 Jul 2017 • Seyed Mehran Kazemi, Angelika Kimmig, Guy Van Den Broeck, David Poole
In this paper, we show that domain recursion can also be applied to models with existential quantifiers.
no code implementations • NeurIPS 2016 • Seyed Mehran Kazemi, Angelika Kimmig, Guy Van Den Broeck, David Poole
Statistical relational models provide compact encodings of probabilistic dependencies in relational domains, but result in highly intractable graphical models.
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.
no code implementations • 14 Jun 2016 • Seyed Mehran Kazemi, David Poole
First-order knowledge compilation techniques have proven efficient for lifted inference.