Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
The overlap gap property: A topological barrier to optimizing over random structures
The problem of optimizing over random structures emerges in many areas of science and
engineering, ranging from statistical physics to machine learning and artificial intelligence …
engineering, ranging from statistical physics to machine learning and artificial intelligence …
Frozen 1-RSB structure of the symmetric Ising perceptron
We prove, under an assumption on the critical points of a real-valued function, that the
symmetric Ising perceptron exhibits thefrozen 1-RSB'structure conjectured by Krauth and …
symmetric Ising perceptron exhibits thefrozen 1-RSB'structure conjectured by Krauth and …
Algorithms and barriers in the symmetric binary perceptron model
The binary (or Ising) perceptron is a toy model of a single-layer neural network and can be
viewed as a random constraint satisfaction problem with a high degree of connectivity. The …
viewed as a random constraint satisfaction problem with a high degree of connectivity. The …
Proof of the contiguity conjecture and lognormal limit for the symmetric perceptron
We consider the symmetric binary perceptron model, a simple model of neural networks that
has gathered significant attention in the statistical physics, information theory and probability …
has gathered significant attention in the statistical physics, information theory and probability …
Learning through atypical phase transitions in overparameterized neural networks
Current deep neural networks are highly overparameterized (up to billions of connection
weights) and nonlinear. Yet they can fit data almost perfectly through variants of gradient …
weights) and nonlinear. Yet they can fit data almost perfectly through variants of gradient …
Geometric barriers for stable and online algorithms for discrepancy minimization
For many computational problems involving randomness, intricate geometric features of the
solution space have been used to rigorously rule out powerful classes of algorithms. This is …
solution space have been used to rigorously rule out powerful classes of algorithms. This is …
[HTML][HTML] Hebbian dreaming for small datasets
The dreaming Hopfield model constitutes a generalization of the Hebbian paradigm for
neural networks, that is able to perform on-line learning when “awake” and also to account …
neural networks, that is able to perform on-line learning when “awake” and also to account …
On the atypical solutions of the symmetric binary perceptron
We study the random binary symmetric perceptron problem, focusing on the behavior of rare
high-margin solutions. While most solutions are isolated, we demonstrate that these rare …
high-margin solutions. While most solutions are isolated, we demonstrate that these rare …
Symmetric perceptron with random labels
The symmetric binary perceptron (SBP) is a random constraint satisfaction problem (CSP)
and a single-layer neural network; it exhibits intriguing features, most notably a sharp phase …
and a single-layer neural network; it exhibits intriguing features, most notably a sharp phase …
How to escape atypical regions in the symmetric binary perceptron: a journey through connected-solutions states
We study the binary symmetric perceptron model, and in particular its atypical solutions.
While the solution-space of this problem is dominated by isolated configurations, it is also …
While the solution-space of this problem is dominated by isolated configurations, it is also …