5 citations · 5 across the 7 of their papers we have counts for
8 papers
In-context learning of solution to simple linear regression task using transformer with masked linear self-attention
Katsuyuki Hagiwara
In-context learning is a remarkable property of transformers and has recently received a lot of interest. In many studies of in-context learning, it has been shown that transformer…
In-context learning of closed form solution to simple linear regression task using transformer with linear self-attention
Katsuyuki Hagiwara
In-context learning is a remarkable property of transformers and has recently received a lot of interest. In many studies of in-context learning, it has been shown that transformer…
An extension of linear self-attention for in-context learning
Katsuyuki Hagiwara
In-context learning is a remarkable property of transformers and has been the focus of recent research. An attention mechanism is a key component in transformers, in which an atten…
A semi-supervised learning using over-parameterized regression
Katsuyuki Hagiwara
Semi-supervised learning (SSL) is an important theme in machine learning, in which we have a few labeled samples and many unlabeled samples. In this paper, for SSL in a regression…
On gradient descent training under data augmentation with on-line noisy copies
Katsuyuki Hagiwara
In machine learning, data augmentation (DA) is a technique for improving the generalization performance. In this paper, we mainly considered gradient descent of linear regression u…
Bridging between soft and hard thresholding by scaling
Katsuyuki Hagiwara
In this article, we developed and analyzed a thresholding method in which soft thresholding estimators are independently expanded by empirical scaling values. The scaling values ha…
On an improvement of LASSO by scaling
Katsuyuki Hagiwara
A sparse modeling is a major topic in machine learning and statistics. LASSO (Least Absolute Shrinkage and Selection Operator) is a popular sparse modeling method while it has been…
Adaptive scaling for soft-thresholding estimator
Katsuyuki Hagiwara
Soft-thresholding is a sparse modeling method that is typically applied to wavelet denoising in statistical signal processing and analysis. It has a single parameter that controls…