How neural networks characterize knowledge: A possible unifying principle for key deep studying phenomena

April 1, 2025

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How neural networks characterize knowledge: A possible unifying principle for key deep studying phenomena

How do neural networks work? It's a query that may confuse novices and consultants alike. A staff from MIT's Pc Science and Synthetic Intelligence Lab (CSAIL) says that understanding these representations, in addition to how they inform the ways in which neural networks be taught from knowledge, is essential for bettering the interpretability, effectivity, and generalizability of deep studying fashions.

With that thoughts, the CSAIL researchers have developed a brand new framework for understanding how representations type in neural networks. Their Canonical Illustration Speculation (CRH) posits that, throughout coaching, neural networks inherently align their latent representations, weights, and neuron gradients inside every layer. This alignment implies that neural networks naturally be taught compact representations based mostly on the diploma and modes of deviation from the CRH.

Senior creator Tomaso Poggio says that, by understanding and leveraging this alignment, engineers can doubtlessly design networks which can be extra environment friendly and simpler to know. The analysis is posted to the arXiv preprint server.

The staff's corresponding Polynomial Alignment Speculation (PAH) posits that, when the CRH is damaged, distinct phases emerge through which the representations, gradients, and weights develop into polynomial capabilities of one another. Poggio says that the CRH and PAH provide a possible unifying principle for key deep studying phenomena resembling neural collapse and the neural function ansatz (NFA).

The brand new CSAIL paper concerning the challenge offers experimental outcomes throughout varied settings to help the CRH and PAH on duties that embrace picture classification and self-supervised studying. The CRH suggests prospects for manually injecting noise into neuron gradients to engineer particular buildings within the mannequin's representations. Poggio says {that a} key future route is to know the circumstances that result in every part and the way these phases have an effect on the conduct and efficiency of fashions.

"The paper affords a brand new perspective on understanding the formation of representations in neural networks by way of the CRH and PAH," says Poggio. "This offers a framework for unifying current observations and guiding future analysis in deep studying."

Co-author Liu Ziyin, a postdoc at CSAIL, says the CRH could clarify sure phenomena in neuroscience, because it implies that neural networks are likely to be taught an orthogonalized illustration, which has been noticed in current mind research. It might even have algorithmic implications: if representations align with the gradients, it is likely to be doable to manually inject noise into neuron gradients to engineer particular buildings within the mannequin's representations.

Ziyin and Poggio co-wrote the paper with professor Isaac Chuang and former postdoc Tomer Galanti, now an assistant professor of pc science at Texas A&M College. They’ll current it later this month on the Worldwide Convention on Studying Representations (ICLR 2025) in Singapore.

Extra data: Liu Ziyin et al, Formation of Representations in Neural Networks, arXiv (2024). DOI: 10.48550/arxiv.2410.03006

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