Measuring the Spectrum of Deepnet Hessians
We apply state-of-the-art tools in modern high-dimensional numerical linear algebra to approximate efficiently the spectrum of the Hessian of modern deepnets, with tens of millions of parameters, trained on real data. We decompose the Hessian into different components and study the dynamics with training and sample size of each term individually.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size
We apply state-of-the-art tools in modern high-dimensional numerical linear algebra to approximate efficiently the spectrum of the Hessian of modern deepnets, with tens of millions of parameters, trained on real data. Ou…
Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians
We consider deep classifying neural networks. We expose a structure in the derivative of the logits with respect to the parameters of the model, which is used to explain the existence of outliers in the spectrum of the H…
Traces of Class/Cross-Class Structure Pervade Deep Learning Spectra
Numerous researchers recently applied empirical spectral analysis to the study of modern deep learning classifiers. We identify and discuss an important formal class/cross-class structure and show how it lies at the orig…
ArticlesDeep LearningEstimating the treatment effect over time under general interference through deep learner integrated TMLE
Understanding the effects of quarantine policies in populations with underlying social networks is crucial for public health, yet most causal inference methods fail here due to their assumption of independent individuals…
Causal InferencecounterfactualADHDeepNet From Raw EEG to Diagnosis: Improving ADHD Diagnosis through Temporal-Spatial Processing, Adaptive Attention Mechanisms, and Explainability in Raw EEG Signals
Attention Deficit Hyperactivity Disorder (ADHD) is a common brain disorder in children that can persist into adulthood, affecting social, academic, and career life. Early diagnosis is crucial for managing these impacts o…
Data Augmentation