Portrait of Perouz Taslakian is unavailable

Perouz Taslakian

Associate Industry Member
Adjunct Professor, McGill University, School of Computer Science
Research Topics
Deep Learning
Multimodal Learning
Vision and Language

Publications

Explaining Graph Neural Networks Using Interpretable Local Surrogates
Farzaneh Heidari
Perouz Taslakian
Guillaume Rabusseau
We propose an interpretable local surrogate (ILS) method for understanding the predictions of black-box graph models. Explainability methods… (see more) are commonly employed to gain insights into black-box models and, given the widespread adoption of GNNs in diverse applications, understanding the underlying reasoning behind their decision-making processes becomes crucial. Our ILS method approximates the behavior of a black-box graph model by fitting a simple surrogate model in the local neighborhood of a given input example. Leveraging the interpretability of the surrogate, ILS is able to identify the most relevant nodes contributing to a specific prediction. To efficiently identify these nodes, we utilize group sparse linear models as local surrogates. Through empirical evaluations on explainability benchmarks, our method consistently outperforms state-of-the-art graph explainability methods. This demonstrates the effectiveness of our approach in providing enhanced interpretability for GNN predictions.
2023-01-01
TAG-ML (published)
proceedings.mlr.press
Using Graph Algorithms to Pretrain Graph Completion Transformers
Jonathan Pilault
Mikhail Galkin
Bahare Fatemi
Perouz Taslakian
David Vasquez
Chris Pal
Recent work on Graph Neural Networks has demonstrated that self-supervised pretraining can further enhance performance on downstream graph, … (see more)link, and node classification tasks. However, the efficacy of pretraining tasks has not been fully investigated for downstream large knowledge graph completion tasks. Using a contextualized knowledge graph embedding approach, we investigate five different pretraining signals, constructed using several graph algorithms and no external data, as well as their combination. We leverage the versatility of our Transformer-based model to explore graph structure generation pretraining tasks (i.e. path and k-hop neighborhood generation), typically inapplicable to most graph embedding methods. We further propose a new path-finding algorithm guided by information gain and find that it is the best-performing pretraining task across three downstream knowledge graph completion datasets. 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. In a multitask setting that combines all pretraining tasks, our method surpasses the latest and strong performing knowledge graph embedding methods on all metrics for FB15K-237, on MRR and Hit@1 for WN18RRand on MRR and hit@10 for JF17K (a knowledge hypergraph dataset).
2022-10-14
ArXiv (preprint)
doi.org
arxiv.org
Object-centric Compositional Imagination for Visual Abstract Reasoning
Rim Assouel
Pau Rodriguez
Perouz Taslakian
David Vazquez
Yoshua Bengio
Like humans devoid of imagination, current machine learning systems lack the ability to adapt to new, unexpected situations by foreseeing th… (see more)em, which makes them unable to solve new tasks by analogical reasoning. In this work, we introduce a new compositional imagination framework that improves a model's ability to generalize. One of the key components of our framework is object-centric inductive biases that enables models to perceive the environment as a series of objects, properties, and transformations. By composing these key ingredients, it is possible to generate new unseen tasks that, when used to train the model, improve generalization. Experiments on a simplified version of the Abstraction and Reasoning Corpus (ARC) demonstrate the effectiveness of our framework.
2022-03-25
ICLR.cc/2022/Workshop/OSC (poster)
openreview.net
openreview.net