It’s easy to write off certain neural networks as “untrainable” when they fall short of modern machine learning tasks. But a team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) reminds us that we just might be wrong. They’ve unravelled a new method known as guidance. It’s a brief phase of alignment that can dramatically enhance the learning ability of previously dismissed neural network architectures.
Traditionally, we’ve considered some network architectures as inherently flawed or limited in dealing with complex tasks. However, these neural networks might be victim to an unfavorable starting point in the parameter space rather than a lack of potential. The researchers found that by pairing these networks with a structured “guide” network for a short while, they could steer the struggling networks towards effective learning.
This technique, unlike knowledge distillation—in which a student model emulates the outputs of a teacher—relies on internal representations. Here, the target network absorbs how the guide network organizes information across its layers instead of mimicking its predictions. Even when the guide network is untrained, this process facilitates a meaningful transfer of knowledge, thereby enhancing the learning ability.
This theory was put to the test using deep fully connected networks (FCNs). The researchers briefly aligned the networks with a guide network using random noise before the actual training. The results were astonishing–notorious for overfitting, these networks became more stable, avoided the usual FCN pitfalls, displayed lower training losses and improved their performances. “It’s impressive that we could use representational similarity to make these traditionally ‘crappy’ networks actually work,” says Vighnesh Subramaniam ’23, MEng ’24, a PhD student in MIT’s Department of Electrical Engineering and Computer Science and lead author of the study.
The study reveals that guidance, unlike knowledge distillation, doesn’t falter when using an untrained teacher network. As guidance relies on the network’s internal structure which carries valuable architectural biases. These biases function like a compass, directing the network towards better learning paths.
But the implications of this research don’t stop at performance improvements. It suggests that the success of a network may be more dependent on its starting point in the learning space than the data it’s trained on. By pairing networks with a guide, the impact of architectural design can be isolated from learned experience. This introduction of guidance provides a new perspective for evaluating network structures’ contribution to effective learning. It also gives scientists a way to understand the differences between architectures, helping refine theories about neural network optimization and identifying which components are consequential for learning.
The highlight, however, is that no network is beyond redemption. Even those once branded as ineffective can be brought up to par with modern standards through guidance. Currently, the CSAIL team is studying which architectural elements are major contributors to these improvements, intending to influence future neural network design.
“It’s generally assumed that different neural network architectures have particular strengths and weaknesses,” noted Leyla Isik, an assistant professor of cognitive science at Johns Hopkins University, who was not involved in the study. “This exciting research shows that one type of network can inherit the advantages of another architecture, without losing its original capabilities.”
The research, a collective effort of Subramaniam and his MIT CSAIL collaborators, was supported by organizations such as the Center for Brains, Minds, and Machines, the National Science Foundation, the MIT-IBM Watson AI Lab, and the U.S. Department of the Air Force Artificial Intelligence Accelerator. Their game-changing findings were recently presented at the Conference and Workshop on Neural Information Processing Systems (NeurIPS).
Read the original article from MIT News here: https://news.mit.edu/2025/guided-learning-lets-untrainable-neural-networks-realize-their-potential-1218
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