Eugene Belilovsky

Understanding Permutation Based Model Merging with Feature Visualizations

Congshu Zou

geraldin nanfack

Stefan Horoi

Linear mode connectivity (LMC) has become a topic of great interest in recent years. It has been empirically demonstrated that popular deep … (voir plus)learning models trained from different initializations exhibit linear model connectivity up to permutation. Based on this, several approaches for finding a permutation of the model's features or weights have been proposed leading to several popular methods for model merging. These methods enable the simple averaging of two models to create a new high-performance model. However, besides accuracy, the properties of these models and their relationships to the representations of the models they derive from are poorly understood. In this work, we study the inner mechanisms behind LMC in model merging through the lens of classic feature visualization methods. Focusing on convolutional neural networks (CNNs) we make several observations that shed light on the underlying mechanisms of model merging by permute and average.

2024-10-10

NeurIPS.cc/2024/Workshop/UniReps (accepté)

openreview.net

Understanding Permutation Based Model Merging with Feature Visualizations

Congshu Zou

geraldin nanfack

Stefan Horoi

Eugene Belilovsky

Linear mode connectivity (LMC) has become a topic of great interest in recent years. It has been empirically demonstrated that popular deep … (voir plus)learning models trained from different initializations exhibit linear model connectivity up to permutation. Based on this, several approaches for finding a permutation of the model's features or weights have been proposed leading to several popular methods for model merging. These methods enable the simple averaging of two models to create a new high-performance model. However, besides accuracy, the properties of these models and their relationships to the representations of the models they derive from are poorly understood. In this work, we study the inner mechanisms behind LMC in model merging through the lens of classic feature visualization methods. Focusing on convolutional neural networks (CNNs) we make several observations that shed light on the underlying mechanisms of model merging by permute and average.

2024-10-10

NeurIPS.cc/2024/Workshop/UniReps (accepté)

openreview.net

WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average

Louis Fournier

Adel Nabli

Masih Aminbeidokhti

Marco Pedersoli

Eugene Belilovsky

Edouard Oyallon

The performance of deep neural networks is enhanced by ensemble methods, which average the output of several models. However, this comes at … (voir plus)an increased cost at inference. Weight averaging methods aim at balancing the generalization of ensembling and the inference speed of a single model by averaging the parameters of an ensemble of models. Yet, naive averaging results in poor performance as models converge to different loss basins, and aligning the models to improve the performance of the average is challenging. Alternatively, inspired by distributed training, methods like DART and PAPA have been proposed to train several models in parallel such that they will end up in the same basin, resulting in good averaging accuracy. However, these methods either compromise ensembling accuracy or demand significant communication between models during training. In this paper, we introduce WASH, a novel distributed method for training model ensembles for weight averaging that achieves state-of-the-art image classification accuracy. WASH maintains models within the same basin by randomly shuffling a small percentage of weights during training, resulting in diverse models and lower communication costs compared to standard parameter averaging methods.

2024-10-10

NeurIPS.cc/2024/Workshop/OPT (publié)

doi.org

openreview.net

Not Only the Last-Layer Features for Spurious Correlations: All Layer Deep Feature Reweighting

Humza Wajid Hameed

G'eraldin Nanfack

Eugene Belilovsky

Spurious correlations are a major source of errors for machine learning models, in particular when aiming for group-level fairness. It has b… (voir plus)een recently shown that a powerful approach to combat spurious correlations is to re-train the last layer on a balanced validation dataset, isolating robust features for the predictor. However, key attributes can sometimes be discarded by neural networks towards the last layer. In this work, we thus consider retraining a classifier on a set of features derived from all layers. We utilize a recently proposed feature selection strategy to select unbiased features from all the layers. We observe this approach gives significant improvements in worst-group accuracy on several standard benchmarks.

2024-09-23

ArXiv (prépublication)

doi.org

arxiv.org

Accelerating Training with Neuron Interaction and Nowcasting Networks

Boris Knyazev

Abhinav Moudgil

Guillaume Lajoie

Eugene Belilovsky

Simon Lacoste-Julien

Neural network training can be accelerated when a learnable update rule is used in lieu of classic adaptive optimizers (e.g. Adam). However,… (voir plus) learnable update rules can be costly and unstable to train and use. Recently, Jang et al. (2023) proposed a simpler approach to accelerate training based on weight nowcaster networks (WNNs). In their approach, Adam is used for most of the optimization steps and periodically, only every few steps, a WNN nowcasts (predicts near future) parameters. We improve WNNs by proposing neuron interaction and nowcasting (NiNo) networks. In contrast to WNNs, NiNo leverages neuron connectivity and graph neural networks to more accurately nowcast parameters. We further show that in some networks, such as Transformers, modeling neuron connectivity accurately is challenging. We address this and other limitations, which allows NiNo to accelerate Adam training by up to 50% in vision and language tasks.

2024-09-06

ArXiv (prépublication)

doi.org

arxiv.org

Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis

Stefan Horoi

Albert Manuel Orozco Camacho

Eugene Belilovsky

Guy Wolf

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

doi.org

openreview.net

Simple and Scalable Strategies to Continually Pre-train Large Language Models

Adam Ibrahim

Benjamin Thérien

Kshitij Gupta

Mats Leon Richter

Quentin Gregory Anthony

Timothee LESORT

Eugene Belilovsky

Irina Rish

2024-07-08

TMLR (accepté)

doi.org

openreview.net

Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis

Stefan Horoi

Albert Manuel Orozco Camacho

Eugene Belilovsky

Guy Wolf

Combining the predictions of multiple trained models through ensembling is generally a good way to improve accuracy by leveraging the differ… (voir plus)ent learned features of the models, however it comes with high computational and storage costs. Model fusion, the act of merging multiple models into one by combining their parameters reduces these costs but doesn't work as well in practice. Indeed, neural network loss landscapes are high-dimensional and non-convex and the minima found through learning are typically separated by high loss barriers. Numerous recent works have been focused on finding permutations matching one network features to the features of a second one, lowering the loss barrier on the linear path between them in parameter space. However, permutations are restrictive since they assume a one-to-one mapping between the different models' neurons exists. We propose a new model merging algorithm, CCA Merge, which is based on Canonical Correlation Analysis and aims to maximize the correlations between linear combinations of the model features. We show that our alignment method leads to better performances than past methods when averaging models trained on the same, or differing data splits. We also extend this analysis into the harder setting where more than 2 models are merged, and we find that CCA Merge works significantly better than past methods. Our code is publicly available at https://github.com/shoroi/align-n-merge

2024-07-07

ArXiv (prépublication)

doi.org

arxiv.org

Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis

Stefan Horoi

Albert Manuel Orozco Camacho

Eugene Belilovsky

Guy Wolf

Combining the predictions of multiple trained models through ensembling is generally a good way to improve accuracy by leveraging the differ… (voir plus)ent learned features of the models, however it comes with high computational and storage costs. Model fusion, the act of merging multiple models into one by combining their parameters reduces these costs but doesn't work as well in practice. Indeed, neural network loss landscapes are high-dimensional and non-convex and the minima found through learning are typically separated by high loss barriers. Numerous recent works have been focused on finding permutations matching one network features to the features of a second one, lowering the loss barrier on the linear path between them in parameter space. However, permutations are restrictive since they assume a one-to-one mapping between the different models' neurons exists. We propose a new model merging algorithm, CCA Merge, which is based on Canonical Correlation Analysis and aims to maximize the correlations between linear combinations of the model features. We show that our alignment method leads to better performances than past methods when averaging models trained on the same, or differing data splits. We also extend this analysis into the harder setting where more than 2 models are merged, and we find that CCA Merge works significantly better than past methods. Our code is publicly available at https://github.com/shoroi/align-n-merge

2024-07-07

ArXiv (prépublication)

doi.org

arxiv.org

Model Breadcrumbs: Scalable Upcycling of Finetuned Foundation Models via Sparse Task Vectors Merging

MohammadReza Davari

Eugene Belilovsky

2024-07-03

ICML.cc/2024/Workshop/FM-Wild (poster)

openreview.net

Simulating federated learning for steatosis detection using ultrasound images

Yue Qi

Pedro Vianna

Alexandre Cadrin-Chênevert

Katleen Blanchet

Emmanuel Montagnon

Eugene Belilovsky

Guy Wolf

Louis-Antoine Mullie

Guy Cloutier

Michael Chassé

An Tang

2024-06-10

Scientific Reports (publié)

doi.org

PETRA: Parallel End-to-end Training with Reversible Architectures

Stephane Rivaud

Louis Fournier

Thomas Pumir

Eugene Belilovsky

Michael Eickenberg

Edouard Oyallon

Reversible architectures have been shown to be capable of performing on par with their non-reversible architectures, being applied in deep l… (voir plus)earning for memory savings and generative modeling. In this work, we show how reversible architectures can solve challenges in parallelizing deep model training. We introduce PETRA, a novel alternative to backpropagation for parallelizing gradient computations. PETRA facilitates effective model parallelism by enabling stages (i.e., a set of layers) to compute independently on different devices, while only needing to communicate activations and gradients between each other. By decoupling the forward and backward passes and keeping a single updated version of the parameters, the need for weight stashing is also removed. We develop a custom autograd-like training framework for PETRA, and we demonstrate its effectiveness on CIFAR-10, ImageNet32, and ImageNet, achieving competitive accuracies comparable to backpropagation using ResNet-18, ResNet-34, and ResNet-50 models.

2024-06-04

ArXiv (prépublication)

doi.org

arxiv.org

Avantage IA

Développement du groupe d'experts de l'ONU sur l'IA

Bourse de recherche en politiques de l'IA de Mila

Avantage IA

Développement du groupe d'experts de l'ONU sur l'IA

Eugene Belilovsky

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Avantage IA

Développement du groupe d'experts de l'ONU sur l'IA

Bourse de recherche en politiques de l'IA de Mila

Avantage IA

Développement du groupe d'experts de l'ONU sur l'IA

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Eugene Belilovsky

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