1 citations · 1 across the 4 of their papers we have counts for
5 papers
Singularities of Non-negative Matrix Factorization and their application to Bayesian inference
Naoki Hayashi, Yota Maeda, Yasushi Esaki
Non-negative matrix factorization (NMF) is a singular statistical model whose Bayesian asymptotics are governed by the real log canonical threshold (RLCT). We study the local geome…
Quantum Algorithms and Hardness for Point-Count Approximation over Finite Fields
Yota Maeda, Hiroshi Yano
We study the approximation of the number of solutions of Laurent polynomials over finite fields. For a Laurent polynomial \[f(x)=\sum_{j=1}^{s}a_jx^{u_j}\in \mathbb{F}_q[x_1^{\pm1}…
Geometric Generality of Transformer-Based Gröbner Basis Computation
Yuta Kambe, Yota Maeda, Tristan Vaccon
The intersection of deep learning and symbolic mathematics has seen rapid progress in recent years, exemplified by the work of Lample and Charton. They demonstrated that effective…
Statistical inference for quantum singular models
Hiroshi Yano, Yota Maeda, Naoki Yamamoto
Deep learning has seen substantial achievements, with numerical and theoretical evidence suggesting that singularities of statistical models are considered a contributing factor to…
MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba
Masakazu Yoshimura, Teruaki Hayashi, Yota Maeda
An ecosystem of Transformer-based models has been established by building large models with extensive data. Parameter-efficient fine-tuning (PEFT) is a crucial technology for deplo…