Equivalence between algorithmic instability and transition to replica
symmetry breaking in perceptron learning systems

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Equivalence between algorithmic instability and transition to replica symmetry breaking in perceptron learning systems

26 November 2021

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Papers citing "Equivalence between algorithmic instability and transition to replica symmetry breaking in perceptron learning systems"

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Title
Learning through atypical "phase transitions" in overparameterized neural networks Carlo Baldassi Clarissa Lauditi Enrico M. Malatesta R. Pacelli Gabriele Perugini R. Zecchina 70 27 0 01 Oct 2021
Proof of the Contiguity Conjecture and Lognormal Limit for the Symmetric Perceptron Emmanuel Abbe Shuangping Li Allan Sly 94 41 0 25 Feb 2021
Statistical physics of unsupervised learning with prior knowledge in neural networks Tianqi Hou Haiping Huang SSL AI4CE 52 11 0 06 Nov 2019
Minimal model of permutation symmetry in unsupervised learning Tianqi Hou K. Y. Michael Wong Haiping Huang 45 20 0 30 Apr 2019
On the role of synaptic stochasticity in training low-precision neural networks Carlo Baldassi Federica Gerace H. Kappen Carlo Lucibello Luca Saglietti Enzo Tartaglione R. Zecchina 41 23 0 26 Oct 2017
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 60 168 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 62 128 0 18 Sep 2015
Origin of the computational hardness for learning with binary synapses Haiping Huang Y. Kabashima 63 56 0 08 Aug 2014