Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Dandi, Yatin"'
A key property of neural networks is their capacity of adapting to data during training. Yet, our current mathematical understanding of feature learning and its relationship to generalization remain limited. In this work, we provide a random matrix a
Externí odkaz:
http://arxiv.org/abs/2410.18938
Autor:
Arnaboldi, Luca, Dandi, Yatin, Krzakala, Florent, Loureiro, Bruno, Pesce, Luca, Stephan, Ludovic
Publikováno v:
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:1730-1762, 2024
We study the impact of the batch size $n_b$ on the iteration time $T$ of training two-layer neural networks with one-pass stochastic gradient descent (SGD) on multi-index target functions of isotropic covariates. We characterize the optimal batch siz
Externí odkaz:
http://arxiv.org/abs/2406.02157
Autor:
Troiani, Emanuele, Dandi, Yatin, Defilippis, Leonardo, Zdeborová, Lenka, Loureiro, Bruno, Krzakala, Florent
Multi-index models - functions which only depend on the covariates through a non-linear transformation of their projection on a subspace - are a useful benchmark for investigating feature learning with neural nets. This paper examines the theoretical
Externí odkaz:
http://arxiv.org/abs/2405.15480
Neural networks can identify low-dimensional relevant structures within high-dimensional noisy data, yet our mathematical understanding of how they do so remains scarce. Here, we investigate the training dynamics of two-layer shallow neural networks
Externí odkaz:
http://arxiv.org/abs/2405.15459
Autor:
Cui, Hugo, Pesce, Luca, Dandi, Yatin, Krzakala, Florent, Lu, Yue M., Zdeborová, Lenka, Loureiro, Bruno
Publikováno v:
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:9662-9695, 2024
In this manuscript, we investigate the problem of how two-layer neural networks learn features from data, and improve over the kernel regime, after being trained with a single gradient descent step. Leveraging the insight from (Ba et al., 2022), we m
Externí odkaz:
http://arxiv.org/abs/2402.04980
Autor:
Dandi, Yatin, Troiani, Emanuele, Arnaboldi, Luca, Pesce, Luca, Zdeborová, Lenka, Krzakala, Florent
Publikováno v:
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:9991-10016, 2024
We investigate the training dynamics of two-layer neural networks when learning multi-index target functions. We focus on multi-pass gradient descent (GD) that reuses the batches multiple times and show that it significantly changes the conclusion ab
Externí odkaz:
http://arxiv.org/abs/2402.03220
The rapid progress in machine learning in recent years has been based on a highly productive connection to gradient-based optimization. Further progress hinges in part on a shift in focus from pattern recognition to decision-making and multi-agent pr
Externí odkaz:
http://arxiv.org/abs/2309.04877
Publikováno v:
Proceedings of the National Academy of Sciences 121.27 (2024): e2311810121
Recent years witnessed the development of powerful generative models based on flows, diffusion or autoregressive neural networks, achieving remarkable success in generating data from examples with applications in a broad range of areas. A theoretical
Externí odkaz:
http://arxiv.org/abs/2308.14085
We investigate theoretically how the features of a two-layer neural network adapt to the structure of the target function through a few large batch gradient descent steps, leading to improvement in the approximation capacity with respect to the initi
Externí odkaz:
http://arxiv.org/abs/2305.18270
We provide a unified analysis of stable local optima of Ising spins with Hamiltonians having pair-wise interactions and partitions in random weighted graphs where a large number of vertices possess sufficient single spin-flip stability. For graphs, w
Externí odkaz:
http://arxiv.org/abs/2305.03591