23 citations · 29 across the 6 of their papers we have counts for
11 papers · 1 filter
Polynomial Speedup in Diffusion Models with the Multilevel Euler-Maruyama Method
Arthur Jacot
We introduce the Multilevel Euler-Maruyama (ML-EM) method compute solutions of SDEs and ODEs using a range of approximators to the drift with increasing accurac…
Saddle-To-Saddle Dynamics in Deep ReLU Networks: Low-Rank Bias in the First Saddle Escape
Ioannis Bantzis, James B. Simon, Arthur Jacot
When a deep ReLU network is initialized with small weights, gradient descent (GD) is at first dominated by the saddle at the origin in parameter space. We study the so-called escap…
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…
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…
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…