Do Modern Problems Require Modern Solutions? This Study Examines the Effectiveness of MLPs (Multi-Layer Perceptions) in Modern Settings

Deep Learning is everywhere. It has revolutionized lots of fields, and the trend seems to continue in the near future. Deep learning is a subfield of machine learning which enables computers to learn and make predictions from large amounts of data. Artificial neural networks are the key component here which are inspired by the structure and functioning of the human brain. 

The ability of deep learning to handle high-dimensional, unstructured data and its remarkable performance in complex tasks have made it a cornerstone of modern AI applications, driving advancements in fields ranging from healthcare and finance to robotics and computer vision.

Deep learning algorithms leverage the power of large-scale computational resources, such as graphics processing units (GPUs), to efficiently train and optimize models with millions or even billions of parameters. Nowadays, these models leverage large, pre-trained models that are fine-tuned for specific tasks. While these models, such as the Transformer architecture for natural language processing and convolutional and transformer-based models for computer vision, have achieved remarkable empirical success, their theoretical understanding lags behind.

The disconnect between theory and practice is growing. This mainly concerns the mathematical simplicity of multi-layer perceptrons (MLPs) that are often used as test beds for analyzing empirical phenomena in more complex models. However, there is a scarcity of empirical data on MLPs trained on benchmark datasets or studies on pre-training/transfer learning. 

This knowledge gap raises a fundamental question: to what extent can MLPs hold on with modern neural network architectures if we were to train them in modern settings? Let’s take a look.

Researchers from ETH Zürich conducted a series of experiments to evaluate MLPs’ performance in modern settings. They also explored the role of inductive bias and the impact of scaling compute resources on the performance of MLPs. By pushing the empirical performance limits of MLPs composed of multiple MLP blocks, the goal here is to bridge the gap between theoretical understanding and practical advancements.

MLPs are known for lacking inductive bias due to their invariance to pixel permutations. Recent advancements, such as the Vision Transformer (ViT) and the MLP-Mixer, challenge the importance of inductive bias in achieving high performance. This study aims to determine whether the absence of inductive bias can be compensated by scaling compute resources, shedding light on the relationship between inductive bias, compute resources, and performance in MLPs.

The study aims to investigate the behavior of MLPs and their empirical performance compared to practical models like Transformers and MLP-Mixers. It also explores the role of inductive bias and the impact of scaling compute resources. The authors hypothesize that MLPs can reflect empirical advances exhibited by practical models and that the lack of inductive bias can be compensated by scaling compute resources.

The research provides several key findings. To start with, it shows that MLPs exhibit similar behavior to their modern counterparts when subjected to scale. Moreover, it highlights the importance of regularization, data augmentation, and the role of SGD’s implicit bias, which differs from CNNs. Surprisingly, larger batch sizes are found to generalize better for MLPs, challenging the role of MLPs as proxies for investigating the implicit bias of SGD. Although the scale employed in this work is insufficient to support the hypothesis of lack of inductive bias being beneficial for performance, it demonstrates that sufficient scale can overcome the bad inductive bias of MLPs, leading to surprisingly strong downstream performance.

In summary, this research demonstrates that even “bad” architectures like MLPs can achieve strong performance when given enough compute resources, with a shift towards investing more compute in dataset size rather than model size.

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