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Boris Knyazev

Visiteur de recherche indépendant - UdeM
Superviseur⋅e principal⋅e
Simon Lacoste-Julien
Sujets de recherche
Apprentissage profond
Optimisation
Réseaux de neurones en graphes
Site web
Google Scholar
GitHub

Publications

Learning Optimizers for Local SGD
Charles-Etienne Joseph
Benjamin Therien
Abhinav Moudgil
Boris Knyazev
Eugene Belilovsky
2023-10-27
NeurIPS.cc/2023/Workshop/Federated_Learning (poster)
openreview.net
openreview.net
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?
Boris Knyazev
Doha Hwang
Simon Lacoste-Julien
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
Learning to Optimize with Recurrent Hierarchical Transformers
Abhinav Moudgil
Boris Knyazev
Guillaume Lajoie
Eugene Belilovsky
2023-06-19
ICML.cc/2023/Workshop/Frontiers4LCD (publié)
openreview.net
openreview.net
Pretrained Language Models to Solve Graph Tasks in Natural Language
Frederik Wenkel
Guy Wolf
Boris Knyazev
Pretrained large language models (LLMs) are powerful learners in a variety of language tasks. We explore if LLMs can learn from graph-struct… (voir plus)ured data when the graphs are described using natural language. We explore data augmentation and pretraining specific to the graph domain and show that LLMs such as GPT-2 and GPT-3 are promising alternatives to graph neural networks.
2023-06-19
ICML.cc/2023/Workshop/SPIGM (poster)
openreview.net
openreview.net
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?
Boris Knyazev
Doha Hwang
Simon Lacoste-Julien
Pretraining a neural network on a large dataset is becoming a cornerstone in machine learning that is within the reach of only a few communi… (voir plus)ties with large-resources. We aim at an ambitious goal of democratizing pretraining. Towards that goal, we train and release a single neural network that can predict high quality ImageNet parameters of other neural networks. By using predicted parameters for initialization we are able to boost training of diverse ImageNet models available in PyTorch. When transferred to other datasets, models initialized with predicted parameters also converge faster and reach competitive final performance.
2023-04-24
ICML.cc/2023/Conference (poster)
doi.org
openreview.net
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?
Boris Knyazev
Doha Hwang
Simon Lacoste-Julien
Pretraining a neural network on a large dataset is becoming a cornerstone in machine learning that is within the reach of only a few communi… (voir plus)ties with large-resources. We aim at an ambitious goal of democratizing pretraining. Towards that goal, we train and release a single neural network that can predict high quality ImageNet parameters of other neural networks. By using predicted parameters for initialization we are able to boost training of diverse ImageNet models available in PyTorch. When transferred to other datasets, models initialized with predicted parameters also converge faster and reach competitive final performance.
2023-03-07
ArXiv (prépublication)
doi.org
arxiv.org
Generative Compositional Augmentations for Scene Graph Prediction
Boris Knyazev
Harm de Vries
Cătălina Cangea
Graham W. Taylor
Aaron Courville
Eugene Belilovsky
Inferring objects and their relationships from an image in the form of a scene graph is useful in many applications at the intersection of v… (voir plus)ision and language. We consider a challenging problem of compositional generalization that emerges in this task due to a long tail data distribution. Current scene graph generation models are trained on a tiny fraction of the distribution corresponding to the most frequent compositions, e.g. . However, test images might contain zero- and few-shot compositions of objects and relationships, e.g. . Despite each of the object categories and the predicate (e.g. ‘on’) being frequent in the training data, the models often fail to properly understand such unseen or rare compositions. To improve generalization, it is natural to attempt increasing the diversity of the training distribution. However, in the graph domain this is non-trivial. To that end, we propose a method to synthesize rare yet plausible scene graphs by perturbing real ones. We then propose and empirically study a model based on conditional generative adversarial networks (GANs) that allows us to generate visual features of perturbed scene graphs and learn from them in a joint fashion. When evaluated on the Visual Genome dataset, our approach yields marginal, but consistent improvements in zero- and few-shot metrics. We analyze the limitations of our approach indicating promising directions for future research.
2021-10-10
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (publié)
doi.org
arxiv.org
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
Boris Knyazev
Harm de Vries
Cătălina Cangea
Graham W. Taylor
Aaron Courville
Eugene Belilovsky
2020-01-01
Proceedings of the British Machine Vision Conference 2020 (publié)
doi.org
arxiv.org
Learning Optimizers for Local SGD
Charles-Etienne Joseph
Benjamin Therien
Abhinav Moudgil
Boris Knyazev
Eugene Belilovsky
Communication-efficient variants of SGD, specifically local SGD, have received a great deal of interest in recent years. These approaches co… (voir plus)mpute multiple gradient steps locally, that is on each worker, before averaging model parameters, helping relieve the critical communication bottleneck in distributed deep learning training. Although many variants of these approaches have been proposed, they can sometimes lag behind state-of-the-art optimizers for deep learning. In this work, we incorporate local optimizers that compute multiple updates into a learned optimization framework, allowing to meta-learn potentially more efficient local SGD algorithms. Our results demonstrate that local learned optimizers can substantially outperform local SGD and its sophisticated variants while maintaining their communication efficiency. We show that the learned optimizers can generalize to new datasets and architectures, demonstrating the potential of learned optimizers for improving communication-efficient distributed learning.
2000-01-01
(publié)
www.semanticscholar.org