Modern big neural networks’ phenomenal results in generalizing new data and tasks have been attributed to their innate capacity to recall intricate training patterns subconsciously. An efficient method for enabling such remembering is to raise the model’s size, although this can significantly increase the expenses of training and serving.
In their new paper ResMem: Learn What You Can and Memorize the Rest, researchers from Stanford University attempt to answer this question by proposing ResMem. This residual-memorization algorithm enhances the generalization ability of smaller neural network models using straight memorization via a distinct k-nearest neighbor component.
Here is a synopsis of the most important findings from the team’s research:
- First, they suggest a two-stage learning approach called residual memorization (ResMem), which combines a basic prediction model with the closest neighbor regressor.
- They provide empirical evidence that ResMem enhances neural networks’ test performance, especially with a large training set.
- In the third paragraph, they theoretically examine the rate of convergence of ResMem on a stylized linear regression issue, demonstrating that it is superior to the baseline prediction model.
Some previous research has found that memorizing the relevant information is sufficient and, in some cases, even essential for efficient generalization in neural network models. In response to this line of inquiry, researchers provide the ResMem method, which employs a unique explicit memorizing strategy to boost the generalization performance of tiny models.
When a conventional neural network has been trained, a soft k-nearest neighbor regressor is fitted to the model’s residuals (rkNN). The combined accuracy of the baseline model and the rkNN determine the final result.
The research team experimented with comparing ResMem to a DeepNet baseline on vision (image classification on CIFAR100 and ImageNet) and NLP (autoregressive language modeling) tasks. As compared to other methods’ generalization abilities on test sets, ResMem performed exceptionally well. The researchers also point out that ResMem provides a more favorable test risk than the baseline predictor when the sample size tends toward infinity.
Modern neural networks may implicitly memorize complicated training patterns, contributing to their excellent generalization performance. Motivated by these findings, scientists are investigating a new strategy for enhancing model generalization through explicit memory. To improve preexisting prediction models (such as neural networks), researchers offer the residual-memorization (ResMem) approach, which uses a k-nearest neighbor-based regressor to fit the model’s residuals. Finally, the fitted residual regressor is added to the original model to get a forecast. ResMem is designed to memorize the training labels explicitly. Researchers demonstrate empirically that, across a range of industry-standard vision and natural language processing benchmarks, ResMem consistently increases the test set generalization of the original prediction model. As a theoretical exercise, they formalize a simplified linear regression issue and thoroughly demonstrate how ResMem improves upon the baseline predictor in terms of test risk.
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Dhanshree Shenwai is a Computer Science Engineer and has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in today’s evolving world making everyone's life easy.