298 citations · 577 across the 14 of their papers we have counts for
14 papers
A Multi-task Supervised Compression Model for Split Computing
Yoshitomo Matsubara, Matteo Mendula, Marco Levorato
Split computing ( split learning) is a promising approach to deep learning models for resource-constrained edge computing systems, where weak sensor (mobile) devices are wire…
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…
torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP
Yoshitomo Matsubara
Reproducibility in scientific work has been becoming increasingly important in research communities such as machine learning, natural language processing, and computer vision commu…
SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing
Niloofar Bahadori, Yoshitomo Matsubara, Marco Levorato +1
Modern IEEE 802.11 (Wi-Fi) networks extensively rely on multiple-input multiple-output (MIMO) to significantly improve throughput. To correctly beamform MIMO transmissions, the acc…
Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages
Shivanshu Gupta, Yoshitomo Matsubara, Ankit Chadha +1
While impressive performance has been achieved on the task of Answer Sentence Selection (AS2) for English, the same does not hold for languages that lack large labeled datasets. In…
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…
Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems
Yoshitomo Matsubara, Luca Soldaini, Eric Lind +1
Large transformer models can highly improve Answer Sentence Selection (AS2) tasks, but their high computational costs prevent their use in many real-world applications. In this pap…
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…
Supervised Compression for Resource-Constrained Edge Computing Systems
Yoshitomo Matsubara, Ruihan Yang, Marco Levorato +1
There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors. However, full-scale deep neural netwo…
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
Yoshitomo Matsubara, Marco Levorato, Francesco Restuccia
Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and spee…
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…
Split Computing for Complex Object Detectors: Challenges and Preliminary Results
Yoshitomo Matsubara, Marco Levorato
Following the trends of mobile and edge computing for DNN models, an intermediate option, split computing, has been attracting attentions from the research community. Previous stud…
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks
Yoshitomo Matsubara, Marco Levorato
The edge computing paradigm places compute-capable devices - edge servers - at the network edge to assist mobile devices in executing data analysis tasks. Intuitively, offloading c…