1.3k citations · 1.6k across the 25 of their papers we have counts for
6 papers · 1 filter
Shallow diffusion networks provably learn hidden low-dimensional structure
Nicholas M. Boffi, Arthur Jacot, Stephen Tu +1
Diffusion-based generative models provide a powerful framework for learning to sample from a complex target distribution. The remarkable empirical success of these models applied t…
Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse
Arthur Jacot, Peter Súkeník, Zihan Wang +1
Deep neural networks (DNNs) at convergence consistently represent the training data in the last layer via a highly symmetric geometric structure referred to as neural collapse. Thi…
How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learning
Arthur Jacot, Seok Hoan Choi, Yuxiao Wen
We show that deep neural networks (DNNs) can efficiently learn any composition of functions with bounded -norm, which allows DNNs to break the curse of dimensionality in way…
Mixed Dynamics In Linear Networks: Unifying the Lazy and Active Regimes
Zhenfeng Tu, Santiago Aranguri, Arthur Jacot
The training dynamics of linear networks are well studied in two distinct setups: the lazy regime and balanced/active regime, depending on the initialization and width of the netwo…
Hamiltonian Mechanics of Feature Learning: Bottleneck Structure in Leaky ResNets
Arthur Jacot, Alexandre Kaiser
We study Leaky ResNets, which interpolate between ResNets and Fully-Connected nets depending on an 'effective depth' hyper-parameter . In the infinite depth limit, we st…
Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning
Yuxiao Wen, Arthur Jacot
We describe the emergence of a Convolution Bottleneck (CBN) structure in CNNs, where the network uses its first few layers to transform the input representation into a representati…