Deep learning is an important and widely used technology that has revolutionized many fields. It is a subfield of machine learning that allows computers to learn and make predictions from large amounts of data. Deep learning relies on artificial neural networks, which are inspired by the structure and function of the human brain.
One of the key strengths of deep learning is its ability to handle complex and unstructured data. It has shown remarkable performance in various tasks, making it a crucial part of modern AI applications. Industries like healthcare, finance, robotics, and computer vision have greatly benefited from deep learning advancements.
Deep learning algorithms require significant computational resources to train and optimize models with millions or even billions of parameters. Graphics processing units (GPUs) are commonly used for this purpose. Nowadays, pre-trained models are also used, which are large models fine-tuned for specific tasks.
While these deep learning models have achieved impressive results, their theoretical understanding is still lacking. This is especially true for multi-layer perceptrons (MLPs), which are used for analyzing complex models. There is a lack of empirical data on MLPs trained on benchmark datasets, as well as studies on pre-training and transfer learning.
To bridge this knowledge gap, researchers from ETH Zürich conducted experiments to evaluate MLPs’ performance in modern settings. They explored the role of inductive bias and the impact of scaling compute resources on MLPs’ performance. The goal was to improve the theoretical understanding of MLPs and their practical applications.
MLPs are known for lacking inductive bias, but recent advancements like the Vision Transformer (ViT) and MLP-Mixer challenge the importance of inductive bias for achieving high performance. The researchers aimed to determine if scaling compute resources can compensate for the absence of inductive bias in MLPs.
The study found that MLPs exhibit similar behavior to modern models when subjected to scale. It also highlighted the importance of regularization, data augmentation, and the role of stochastic gradient descent (SGD)’s implicit bias, which differs from convolutional neural networks (CNNs). Surprisingly, the study found that larger batch sizes actually generalize better for MLPs, challenging the common belief about their limitations.
Overall, the research showed that even “bad” architectures like MLPs can achieve strong performance with enough compute resources. It suggests that investing more compute resources in dataset size rather than model size is beneficial. The findings contribute to a better understanding of MLPs and their potential in practical applications.
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Ekrem Çetinkaya, a researcher who has a background in deep learning, computer vision, video encoding, and multimedia networking, contributed to this article.
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