1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2025
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…
quant-ph2024★ 1 cited
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…
cs.CL2024
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…