13 citations · 33 across the 25 of their papers we have counts for
8 papers · 1 filter
Algebraic Approach to Ridge-Regularized Mean Squared Error Minimization in Minimal ReLU Neural Network
Ryoya Fukasaku, Yutaro Kabata, Akifumi Okuno
This paper investigates a perceptron, a simple neural network model, with ReLU activation and a ridge-regularized mean squared error (RR-MSE). Our approach leverages the fact that…
Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction
Ruixing Cao, Akifumi Okuno, Kei Nakagawa +1
For supervised classification problems, this paper considers estimating the query's label probability through local regression using observed covariates. Well-known nonparametric k…
A generalization gap estimation for overparameterized models via the Langevin functional variance
Akifumi Okuno, Keisuke Yano
This paper discusses the estimation of the generalization gap, the difference between generalization performance and training performance, for overparameterized models including ne…
Extrapolation Towards Imaginary -Nearest Neighbour and Its Improved Convergence Rate
Akifumi Okuno, Hidetoshi Shimodaira
-nearest neighbour (-NN) is one of the simplest and most widely-used methods for supervised classification, that predicts a query's label by taking weighted ratio of observed…
Robust Graph Embedding with Noisy Link Weights
Akifumi Okuno, Hidetoshi Shimodaira
We propose -graph embedding for robustly learning feature vectors from data vectors and noisy link weights. A newly introduced empirical moment -score reduces the influence o…
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability
Akifumi Okuno, Geewook Kim, Hidetoshi Shimodaira
We propose shifted inner-product similarity (SIPS), which is a novel yet very simple extension of the ordinary inner-product similarity (IPS) for neural-network based graph embeddi…
On representation power of neural network-based graph embedding and beyond
Akifumi Okuno, Hidetoshi Shimodaira
We consider the representation power of siamese-style similarity functions used in neural network-based graph embedding. The inner product similarity (IPS) with feature vectors com…
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
Akifumi Okuno, Tetsuya Hada, Hidetoshi Shimodaira
A simple framework Probabilistic Multi-view Graph Embedding (PMvGE) is proposed for multi-view feature learning with many-to-many associations so that it generalizes various existi…