Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes

30 June 2017

Papers citing "Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes"

8 / 8 papers shown

Title
DC is all you need: describing ReLU from a signal processing standpoint Christodoulos Kechris Jonathan Dan Jose Miranda David Atienza 41 3 0 23 Jul 2024
Information-Theoretic Generalization Bounds for Deep Neural Networks Haiyun He Christina Lee Yu 75 5 0 04 Apr 2024
Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks Zhiwei Bai Yaoyu Zhang Z. Xu Yaoyu Zhang 43 6 0 26 May 2022
Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond Levent Sagun Léon Bottou Yann LeCun UQCV 55 233 0 22 Nov 2016
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys Pratik Chaudhari A. Choromańska Stefano Soatto Yann LeCun Carlo Baldassi C. Borgs J. Chayes Levent Sagun R. Zecchina ODL 63 769 0 06 Nov 2016
Topology and Geometry of Half-Rectified Network Optimization C. Freeman Joan Bruna 101 235 0 04 Nov 2016
Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes Carlo Baldassi C. Borgs J. Chayes Alessandro Ingrosso Carlo Lucibello Luca Saglietti R. Zecchina 40 166 0 20 May 2016
Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses Carlo Baldassi Alessandro Ingrosso Carlo Lucibello Luca Saglietti R. Zecchina 32 127 0 18 Sep 2015