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deep-learningICLR
2024
Shangmin Guo, Yi Ren, Stefano V. Albrecht, Kenny Smith
lpNTK: Better Generalisation with Less Data via Sample Interaction During Learning
International Conference on Learning Representations, 2024
Abstract | BibTex | arXiv | Code
ICLRdeep-learning
Abstract:
Although much research has been done on proposing new models or loss functions to improve the generalisation of artificial neural networks (ANNs), less attention has been directed to the impact of the training data on generalisation. In this work, we start from approximating the interaction between samples, i.e. how learning one sample would modify the model's prediction on other samples. Through analysing the terms involved in weight updates in supervised learning, we find that labels influence the interaction between samples. Therefore, we propose the labelled pseudo Neural Tangent Kernel (lpNTK) which takes label information into consideration when measuring the interactions between samples. We first prove that lpNTK asymptotically converges to the empirical neural tangent kernel in terms of the Frobenius norm under certain assumptions. Secondly, we illustrate how lpNTK helps to understand learning phenomena identified in previous work, specifically the learning difficulty of samples and forgetting events during learning. Moreover, we also show that using lpNTK to identify and remove poisoning training samples does not hurt the generalisation performance of ANNs.
@inproceedings{guo2024lpntk,
title={Sample Relationship from Learning Dynamics Matters for Generalisation},
author={Shangmin Guo and Yi Ren and Stefano V. Albrecht and Kenny Smith},
booktitle={12th International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=8Ju0VmvMCW}
}
2023
Yi Ren, Shangmin Guo, Wonho Bae, Danica J. Sutherland
How to Prepare Your Task Head for Finetuning
International Conference on Learning Representations, 2023
Abstract | BibTex | arXiv
ICLRdeep-learningtransfer-learning
Abstract:
In the era of deep learning, transferring information from a pretrained network to a downstream task by finetuning has many benefits. The choice of task head plays an important role in fine-tuning, as the pretrained and downstream tasks are usually different. Although there exist many different designs for finetuning, a full understanding of when and why these algorithms work has been elusive. We analyze how the choice of task head controls feature adaptation and hence influences the downstream performance. By decomposing the feature's learning dynamics, we find the key aspect is the training accuracy and loss at the beginning of finetuning, which determines the "energy" available for the feature's adaptation. We identify a significant trend in the effect of changes in this initial energy on the resulting features after finetuning. Specifically, as the energy increases, the Euclidean and cosine distances between the resulting and original features increase, while their dot product (and the resulting features’ norm) first increases and then decreases. Inspired by this, we give several practical principles that lead to better downstream performance. We analytically prove this trend in an overparamterized linear setting and verify its applicability to different experimental settings.
@inproceedings{ ren2023how,
title={How to Prepare Your Task Head for Finetuning},
author={Yi Ren and Shangmin Guo and Wonho Bae and Danica J. Sutherland},
booktitle={International Conference on Learning Representations (ICLR)},
year={2023},
url={https://openreview.net/forum?id=gVOXZproe-e}
}