298 citations · 577 across the 14 of their papers we have counts for
5 papers · 1 filter
A Transformer Model for Symbolic Regression towards Scientific Discovery
Florian Lalande, Yoshitomo Matsubara, Naoya Chiba +3
Symbolic Regression (SR) searches for mathematical expressions which best describe numerical datasets. This allows to circumvent interpretation issues inherent to artificial neural…
Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
Yoshitomo Matsubara, Naoya Chiba, Ryo Igarashi +1
This paper revisits datasets and evaluation criteria for Symbolic Regression (SR), specifically focused on its potential for scientific discovery. Focused on a set of formulas used…
SC2 Benchmark: Supervised Compression for Split Computing
Yoshitomo Matsubara, Ruihan Yang, Marco Levorato +1
With the increasing demand for deep learning models on mobile devices, splitting neural network computation between the device and a more powerful edge server has become an attract…
BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing
Yoshitomo Matsubara, Davide Callegaro, Sameer Singh +2
Although mission-critical applications require the use of deep neural networks (DNNs), their continuous execution at mobile devices results in a significant increase in energy cons…
torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation
Yoshitomo Matsubara
While knowledge distillation (transfer) has been attracting attentions from the research community, the recent development in the fields has heightened the need for reproducible st…