Aristide Baratin

Torque-Aware Momentum

Goncalo Mordido

Gintare Karolina Dziugaite

Razvan Pascanu

Efficiently exploring complex loss landscapes is key to the performance of deep neural networks. While momentum-based optimizers are widely … (voir plus)used in state-of-the-art setups, classical momentum can still struggle with large, misaligned gradients, leading to oscillations. To address this, we propose Torque-Aware Momentum (TAM), which introduces a damping factor based on the angle between the new gradients and previous momentum, stabilizing the update direction during training. Empirical results show that TAM, which can be combined with both SGD and Adam, enhances exploration, handles distribution shifts more effectively, and improves generalization performance across various tasks, including image classification and large language model fine-tuning, when compared to classical momentum-based optimizers.

2024-12-25

ArXiv (prépublication)

arxiv.org

Torque-Aware Momentum

Goncalo Mordido

Gintare Karolina Dziugaite

Razvan Pascanu

2024-12-25

ArXiv (prépublication)

arxiv.org

Torque-Aware Momentum

Goncalo Mordido

Gintare Karolina Dziugaite

Razvan Pascanu

Efficiently exploring complex loss landscapes is key to the performance of deep neural networks. While momentum-based optimizers are widely … (voir plus)used in state-of-the-art setups, classical momentum can still struggle with large, misaligned gradients, leading to oscillations. To address this, we propose Torque-Aware Momentum (TAM), which introduces a damping factor based on the angle between the new gradients and previous momentum, stabilizing the update direction during training. Empirical results show that TAM, which can be combined with both SGD and Adam, enhances exploration, handles distribution shifts more effectively, and improves generalization performance across various tasks, including image classification and large language model fine-tuning, when compared to classical momentum-based optimizers.

2024-12-25

ArXiv (prépublication)

arxiv.org

How Learning Rates Shape Neural Network Focus: Insights from Example Ranking

Ekaterina Lobacheva

Keller Jordan

Nicolas Le Roux

The learning rate is a key hyperparameter that affects both the speed of training and the generalization performance of neural networks. Th… (voir plus)rough a new {\it loss-based example ranking} analysis, we show that networks trained with different learning rates focus their capacity on different parts of the data distribution, leading to solutions with different generalization properties. These findings, which hold across architectures and datasets, provide new insights into how learning rates affect model performance and example-level dynamics in neural networks.

2024-10-10

NeurIPS.cc/2024/Workshop/SciForDL (poster)

Lookbehind-SAM: k steps back, 1 step forward

Goncalo Mordido

2024-07-08

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

proceedings.mlr.press

Unsupervised Concept Discovery Mitigates Spurious Correlations

Md Rifat Arefin

Yang Zhang

Francesco Locatello

Irina Rish

Dianbo Liu

Kenji Kawaguchi

2024-07-08

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

Promoting Exploration in Memory-Augmented Adam using Critical Momenta

Goncalo Mordido

Adaptive gradient-based optimizers, particularly Adam, have left their mark in training large-scale deep learning models. The strength of su… (voir plus)ch optimizers is that they exhibit fast convergence while being more robust to hyperparameter choice. However, they often generalize worse than non-adaptive methods. Recent studies have tied this performance gap to flat minima selection: adaptive methods tend to find solutions in sharper basins of the loss landscape, which in turn hurts generalization. To overcome this issue, we propose a new memory-augmented version of Adam that promotes exploration towards flatter minima by using a buffer of critical momentum terms during training. Intuitively, the use of the buffer makes the optimizer overshoot outside the basin of attraction if it is not wide enough. We empirically show that our method improves the performance of several variants of Adam on standard supervised language modelling and image classification tasks.

2024-06-09

TMLR (accepté)

How connectivity structure shapes rich and lazy learning in neural circuits

Yuhan Helena Liu

Jonathan Cornford

Stefan Mihalas

Eric Todd SheaBrown

Guillaume Lajoie

In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learn… (voir plus)ing dynamics. Notably, initial weight distributions with small (resp. large) variance may yield a rich (resp. lazy) regime, where significant (resp. minor) changes to network states and representation are observed over the course of learning. However, in biology, neural circuit connectivity generally has a low-rank structure and therefore differs markedly from the random initializations generally used for these studies. As such, here we investigate how the structure of the initial weights — in particular their effective rank — influences the network learning regime. Through both empirical and theoretical analyses, we discover that high-rank initializations typically yield smaller network changes indicative of lazier learning, a finding we also confirm with experimentally-driven initial connectivity in recurrent neural networks. Conversely, low-rank initialization biases learning towards richer learning. Importantly, however, as an exception to this rule, we find lazier learning can still occur with a low-rank initialization that aligns with task and data statistics. Our research highlights the pivotal role of initial weight structures in shaping learning regimes, with implications for metabolic costs of plasticity and risks of catastrophic forgetting.

2024-01-16

ICLR.cc/2024/Conference (poster)

CrossSplit: Mitigating Label Noise Memorization through Data Splitting

Jihye Kim

Yang Zhang

Simon Lacoste-Julien

We approach the problem of improving robustness of deep learning algorithms in the presence of label noise. Building upon existing label cor… (voir plus)rection and co-teaching methods, we propose a novel training procedure to mitigate the memorization of noisy labels, called CrossSplit, which uses a pair of neural networks trained on two disjoint parts of the labeled dataset. CrossSplit combines two main ingredients: (i) Cross-split label correction. The idea is that, since the model trained on one part of the data cannot memorize example-label pairs from the other part, the training labels presented to each network can be smoothly adjusted by using the predictions of its peer network; (ii) Cross-split semi-supervised training. A network trained on one part of the data also uses the unlabeled inputs of the other part. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and mini-WebVision datasets demonstrate that our method can outperform the current state-of-the-art in a wide range of noise ratios. The project page is at https://rlawlgul.github.io/.

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (publié)