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Yoshiyuki Kabashima

7 accepted papers

2026

Dynamical properties of dense associative memory

ICLR 2026poster

Dense associative memory, a fundamental instance of modern Hopfield networks, can store a large number of memory patterns as equilibrium states of recurrent networks. While the stationary-state storage capacity has been investigated, its dynamical properties have not yet been discussed. In this pape…

Cited by 0SourceScholar
2024

QCS-SGM+: Improved Quantized Compressed Sensing with Score-Based Generative Models

AAAI 2024technical

In practical compressed sensing (CS), the obtained measurements typically necessitate quantization to a limited number of bits prior to transmission or storage. This nonlinear quantization process poses significant recovery challenges, particularly with extreme coarse quantization such as 1-bit. Rec…

2023

Average case analysis of Lasso under ultra sparse conditions

AISTATS 2023poster

We analyze the performance of the least absolute shrinkage and selection operator (Lasso) for the linear model when the number of regressors $N$ grows larger keeping the true support size $d$ finite, i.e., the ultra-sparse case. The result is based on a novel treatment of the non-rigorous replica me…

Cited by 8SourcePDFScholar
2023

On Model Selection Consistency of Lasso for High-Dimensional Ising Models

AISTATS 2023poster

We theoretically analyze the model selection consistency of least absolute shrinkage and selection operator (Lasso), both with and without post-thresholding, for high-dimensional Ising models. For random regular (RR) graphs of size $p$ with regular node degree $d$ and uniform couplings $\theta_0$, i…

Cited by 1SourcePDFScholar
2021

Ising Model Selection Using $\ell_{1}$-Regularized Linear Regression: A Statistical Mechanics Analysis

NeurIPS 2021poster

We theoretically analyze the typical learning performance of $\ell_{1}$-regularized linear regression ($\ell_1$-LinR) for Ising model selection using the replica method from statistical mechanics. For typical random regular graphs in the paramagnetic phase, an accurate estimate of the typical sample…

Cited by 2SourcePDFScholar
2018

Objective and efficient inference for couplings in neuronal networks

NeurIPS 2018poster

Inferring directional couplings from the spike data of networks is desired in various scientific fields such as neuroscience. Here, we apply a recently proposed objective procedure to the spike data obtained from the Hodgkin-Huxley type models and in vitro neuronal networks cultured in a circular st…

Cited by 7SourcePDFScholar