1 citations · 3 across the 10 of their papers we have counts for
11 papers
Fault-tolerant foundation models
Trevor McCourt, Ila R. Fiete, Isaac L. Chuang
Emerging computer hardware often trades reliability for energy efficiency; here we show that large-language models (LLMs) can be trained to tolerate this unreliability, and that ra…
Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws
Liu Ziyin, Yizhou Xu, Tomaso Poggio +1
Neural networks trained by gradient descent on a smooth cost function can nevertheless learn in steps: the cost holds on long plateaus and then drops abruptly. Meanwhile, training…
Does SGD Seek Flatness or Sharpness? An Exactly Solvable Model
Yizhou Xu, Pierfrancesco Beneventano, Isaac Chuang +1
A large body of theory and empirical work hypothesizes a connection between the flatness of a neural network's loss landscape during training and its performance. However, there ha…
Topological Invariance and Breakdown in Learning
Yongyi Yang, Tomaso Poggio, Isaac Chuang +1
We prove that for a broad class of permutation-equivariant learning rules (including SGD, Adam, and others), the training process induces a bi-Lipschitz mapping between neurons and…
A universal compression theory for lottery ticket hypothesis and neural scaling laws
Hong-Yi Wang, Di Luo, Tomaso Poggio +2
When training large-scale models, the performance typically scales with the number of parameters and the dataset size according to a slow power law. A fundamental theoretical and p…
Proof of a perfect platonic representation hypothesis
Liu Ziyin, Isaac Chuang
In this note, we elaborate on and explain in detail the proof given by Ziyin et al. (2025) of the ``perfect" Platonic Representation Hypothesis (PRH) for the embedded deep linear n…
Heterosynaptic Circuits Are Universal Gradient Machines
Liu Ziyin, Isaac Chuang, Tomaso Poggio
We propose a design principle for the learning circuits of the biological brain. The principle states that almost any dendritic weights updated via heterosynaptic plasticity can im…
Neural Thermodynamics: Entropic Forces in Deep and Universal Representation Learning
Liu Ziyin, Yizhou Xu, Isaac Chuang
With the rapid discovery of emergent phenomena in deep learning and large language models, understanding their cause has become an urgent need. Here, we propose a rigorous entropic…
Parameter Symmetry Potentially Unifies Deep Learning Theory
Liu Ziyin, Yizhou Xu, Tomaso Poggio +1
The dynamics of learning in modern large AI systems is hierarchical, often characterized by abrupt, qualitative shifts akin to phase transitions observed in physical systems. While…
Formation of Representations in Neural Networks
Liu Ziyin, Isaac Chuang, Tomer Galanti +1
Understanding neural representations will help open the black box of neural networks and advance our scientific understanding of modern AI systems. However, how complex, structured…
Remove Symmetries to Control Model Expressivity and Improve Optimization
Liu Ziyin, Yizhou Xu, Isaac Chuang
When symmetry is present in the loss function, the model is likely to be trapped in a low-capacity state that is sometimes known as a "collapse". Being trapped in these low-capacit…